{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from hyperts.datasets import load_real_known_cause_dataset\n",
    "\n",
    "from hyperts.framework.dl.models import ConvVAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Anmoly Detection Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_real_known_cause_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-02 21:15:00</td>\n",
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       "      <td>2013-12-02 21:20:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-02 21:25:00</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-02 21:30:00</td>\n",
       "      <td>78.140707</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-02 21:35:00</td>\n",
       "      <td>79.329836</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp      value  anomaly\n",
       "0  2013-12-02 21:15:00  73.967322        0\n",
       "1  2013-12-02 21:20:00  74.935882        0\n",
       "2  2013-12-02 21:25:00  76.124162        0\n",
       "3  2013-12-02 21:30:00  78.140707        0\n",
       "4  2013-12-02 21:35:00  79.329836        0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pop ground truth from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = data.pop('anomaly') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>73.967322</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-02 21:20:00</td>\n",
       "      <td>74.935882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-02 21:25:00</td>\n",
       "      <td>76.124162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-02 21:30:00</td>\n",
       "      <td>78.140707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-02 21:35:00</td>\n",
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      ],
      "text/plain": [
       "             timestamp      value\n",
       "0  2013-12-02 21:15:00  73.967322\n",
       "1  2013-12-02 21:20:00  74.935882\n",
       "2  2013-12-02 21:25:00  76.124162\n",
       "3  2013-12-02 21:30:00  78.140707\n",
       "4  2013-12-02 21:35:00  79.329836"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['timestamp'] = pd.to_datetime(data['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22683 entries, 0 to 22682\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   timestamp  22683 non-null  datetime64[ns]\n",
      " 1   value      22683 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 354.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### split train_data and test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "detect_length = 15000\n",
    "\n",
    "train_data, test_data = train_test_split(data, test_size=detect_length, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_data[['timestamp']]\n",
    "train_y = train_data[['value']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### tranformer indices with StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train_y['value'] = sc.fit_transform(train_y['value'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### employ VAE to do anomaly detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Initial network and fit. (X and y should be a DataFrame, where X contains ``timestamp`` and ``covariates`` columns, and y contains ``indices`` columns.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae = ConvVAE(task='univariate-detection', \n",
    "              timestamp='timestamp',\n",
    "              conv_type='separable',\n",
    "              window=24,\n",
    "              latent_dim=4,\n",
    "              cnn_filters=64,\n",
    "              learning_rate=0.0005,\n",
    "              drop_rate=0.2,\n",
    "              nb_layers=3,\n",
    "              kernel_size=1,\n",
    "              out_activation='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ConvVAE\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_continuous_vars_all (Inpu [(None, 24, 1)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder_conv1d_0 (SeparableConv (None, 24, 64)       129         input_continuous_vars_all[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "encoder_dropout_rate0.2_0 (Drop (None, 24, 64)       0           encoder_conv1d_0[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "encoder_conv1d_1 (SeparableConv (None, 24, 32)       2144        encoder_dropout_rate0.2_0[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "encoder_dropout_rate0.2_1 (Drop (None, 24, 32)       0           encoder_conv1d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "encoder_conv1d_2 (SeparableConv (None, 24, 16)       560         encoder_dropout_rate0.2_1[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "encoder_dropout_rate0.2_2 (Drop (None, 24, 16)       0           encoder_conv1d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "encoder_flatten_conv (Flatten)  (None, 384)          0           encoder_dropout_rate0.2_2[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "encoder_hidden_dense (Dense)    (None, 64)           24640       encoder_flatten_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "z_mean (Dense)                  (None, 4)            260         encoder_hidden_dense[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "z_log_var (Dense)               (None, 4)            260         encoder_hidden_dense[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "sampling (Sampling)             (None, 4)            0           z_mean[0][0]                     \n",
      "                                                                 z_log_var[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_inter (Dense)           (None, 384)          1920        sampling[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "decoder_reshape_inter (Reshape) (None, 24, 16)       0           decoder_inter[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "decoder_conv1dtranspose_0 (Conv (None, 24, 16)       272         decoder_reshape_inter[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_dropout_rate0.2_0 (Drop (None, 24, 16)       0           decoder_conv1dtranspose_0[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_conv1dtranspose_1 (Conv (None, 24, 32)       544         decoder_dropout_rate0.2_0[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_dropout_rate0.2_1 (Drop (None, 24, 32)       0           decoder_conv1dtranspose_1[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_conv1dtranspose_2 (Conv (None, 24, 64)       2112        decoder_dropout_rate0.2_1[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_dropout_rate0.2_2 (Drop (None, 24, 64)       0           decoder_conv1dtranspose_2[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_outputs (Conv1DTranspos (None, 24, 1)        65          decoder_dropout_rate0.2_2[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_activation_linear (Acti (None, 24, 1)        0           decoder_outputs[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_AddV2 (TensorFlowOp [(None, 4)]          0           z_log_var[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Square_1 (TensorFlo [(None, 4)]          0           z_mean[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sub_1 (TensorFlowOp [(None, 4)]          0           tf_op_layer_AddV2[0][0]          \n",
      "                                                                 tf_op_layer_Square_1[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Exp (TensorFlowOpLa [(None, 4)]          0           z_log_var[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_strided_slice (Tens [(None, 24, 1)]      0           input_continuous_vars_all[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sub_2 (TensorFlowOp [(None, 4)]          0           tf_op_layer_Sub_1[0][0]          \n",
      "                                                                 tf_op_layer_Exp[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sub (TensorFlowOpLa [(None, 24, 1)]      0           tf_op_layer_strided_slice[0][0]  \n",
      "                                                                 decoder_activation_linear[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sum_1 (TensorFlowOp [(None,)]            0           tf_op_layer_Sub_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Square (TensorFlowO [(None, 24, 1)]      0           tf_op_layer_Sub[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mul (TensorFlowOpLa [(None,)]            0           tf_op_layer_Sum_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sum (TensorFlowOpLa [(None,)]            0           tf_op_layer_Square[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mul_1 (TensorFlowOp [(None,)]            0           tf_op_layer_Mul[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_AddV2_1 (TensorFlow [(None,)]            0           tf_op_layer_Sum[0][0]            \n",
      "                                                                 tf_op_layer_Mul_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mean (TensorFlowOpL [()]                 0           tf_op_layer_AddV2_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_loss (AddLoss)              ()                   0           tf_op_layer_Mean[0][0]           \n",
      "==================================================================================================\n",
      "Total params: 32,906\n",
      "Trainable params: 32,906\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118/118 [==============================] - 2s 15ms/step - loss: 25.4331 - mse: 1.0595 - val_loss: 11.8271 - val_mse: 0.4904\n",
      "Epoch 2/100\n",
      "118/118 [==============================] - 1s 8ms/step - loss: 14.4801 - mse: 0.5729 - val_loss: 5.5232 - val_mse: 0.2051\n",
      "Epoch 3/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 5.3427 - mse: 0.1916 - val_loss: 2.2826 - val_mse: 0.0694\n",
      "Epoch 4/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 3.7473 - mse: 0.1268 - val_loss: 2.1318 - val_mse: 0.0642\n",
      "Epoch 5/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 3.2849 - mse: 0.1088 - val_loss: 2.2535 - val_mse: 0.0702\n",
      "Epoch 6/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 3.0341 - mse: 0.0989 - val_loss: 2.2821 - val_mse: 0.0728\n",
      "Epoch 7/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 2.6637 - mse: 0.0856 - val_loss: 2.2649 - val_mse: 0.0742\n",
      "Epoch 8/100\n",
      "118/118 [==============================] - 1s 7ms/step - loss: 2.6251 - mse: 0.0847 - val_loss: 2.2526 - val_mse: 0.0741\n",
      "Epoch 9/100\n",
      "113/118 [===========================>..] - ETA: 0s - loss: 2.5305 - mse: 0.0821\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "118/118 [==============================] - 1s 9ms/step - loss: 2.5292 - mse: 0.0821 - val_loss: 2.4818 - val_mse: 0.0851\n",
      "Epoch 10/100\n",
      "118/118 [==============================] - 1s 9ms/step - loss: 2.4069 - mse: 0.0779 - val_loss: 2.5063 - val_mse: 0.0866\n",
      "Epoch 11/100\n",
      "118/118 [==============================] - 1s 10ms/step - loss: 2.2877 - mse: 0.0733 - val_loss: 2.4580 - val_mse: 0.0851\n",
      "Epoch 12/100\n",
      "118/118 [==============================] - 1s 9ms/step - loss: 2.3730 - mse: 0.0778 - val_loss: 2.5133 - val_mse: 0.0882\n",
      "Epoch 13/100\n",
      "118/118 [==============================] - 1s 10ms/step - loss: 2.2606 - mse: 0.0737 - val_loss: 2.6199 - val_mse: 0.0928\n",
      "Epoch 14/100\n",
      "115/118 [============================>.] - ETA: 0s - loss: 2.0760 - mse: 0.0665\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "118/118 [==============================] - 1s 10ms/step - loss: 2.0726 - mse: 0.0664 - val_loss: 2.6399 - val_mse: 0.0943\n",
      "Epoch 00014: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x26fa7478a20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae.fit(X=train_X, y=train_y, epochs=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### obtain the train predicted labels and the position of outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = vae.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_outliers = np.where(train_labels == 1)[0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### obtain the test predicted labels and the position of outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = test_data[['timestamp']]\n",
    "test_y = test_data[['value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y['value'] = sc.transform(test_y['value'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels = vae.predict_outliers(X=test_X, y=test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_outliers = np.where(test_labels == 1)[0].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(data['timestamp'], data['value'], c='gray')\n",
    "plt.scatter(train_data.loc[train_outliers, 'timestamp'], train_data.loc[train_outliers, 'value'], c='r', label='train anomaly')\n",
    "plt.scatter(test_data.loc[test_outliers, 'timestamp'], test_data.loc[test_outliers, 'value'], c='b', label='test anomaly')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HyperTS - make_experiment API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperts import make_experiment\n",
    "from hyperts.toolbox import temporal_train_test_split\n",
    "from hyperts.datasets import load_real_known_cause_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_real_known_cause_dataset()\n",
    "target = data.pop('anomaly') \n",
    "\n",
    "detect_length = 15000\n",
    "train_data, test_data = temporal_train_test_split(data, test_size=detect_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "experiment = make_experiment(\n",
    "    train_data=train_data.copy(),\n",
    "    task='detection',\n",
    "    timestamp='timestamp',\n",
    "    mode='dl',\n",
    "    max_trials=3,\n",
    "    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = experiment.run(epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X, _ = model.split_X_y(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7683</th>\n",
       "      <td>2013-12-29 13:30:00</td>\n",
       "      <td>82.032343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7684</th>\n",
       "      <td>2013-12-29 13:35:00</td>\n",
       "      <td>80.900366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7685</th>\n",
       "      <td>2013-12-29 13:40:00</td>\n",
       "      <td>81.031536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>2013-12-29 13:45:00</td>\n",
       "      <td>80.200337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7687</th>\n",
       "      <td>2013-12-29 13:50:00</td>\n",
       "      <td>80.182448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp      value\n",
       "7683  2013-12-29 13:30:00  82.032343\n",
       "7684  2013-12-29 13:35:00  80.900366\n",
       "7685  2013-12-29 13:40:00  81.031536\n",
       "7686  2013-12-29 13:45:00  80.200337\n",
       "7687  2013-12-29 13:50:00  80.182448"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X=test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xe4SPsrIyVFVVoXfv3mhpacGcOXMwfvx4XH755Thy5AjmzJmDDh06oGPHjvj888+RkJCAP/zhD6iqqsLcuXORk5MT9L7ZtGkTrrjiChQXF2P+/Pn45ptvcOGFF6KhoQHvvfcepkyZgj59+mDlypVYtWqVKQHEZrOhT58+2LlzJwBAkiQMHToU06dPh8vlwoIFC/D555/jkksuwcSJE7Fv3z6fVBetfXHCCScgKysr6G0mCIIgCIJoT3z88cddjh49mhbOdXTs2LFhypQp+6xe7l133dX1lltuOXLbbbdVVFdX21atWpUKAEuWLNnWt2/fQdXV1Wt55MYzzzyT/8477+QvXrx4W0lJiWP69Ondr7vuuq7z58/fzZe3bNmyjG3btm222WzSgQMHfEI+Lr744u7p6emuzZs3b87KynItXrw4HQCcTieuvvrq8gULFuxyOBy47LLLSm+66aauixcv3mn19kYKEj4Iog3S0NCAtLQ01XSPjIwMHDp0yPO+uLgY/fv3BwCceOKJWLFiBfbv34/MzEw4HA6UlZUhLS3NR2xYvXo1zjrrLE9nfPz48XjmmWdw/vnne+YZP348kpKSAAB9+/bFZ599hqqqKuTk5GDDhg3o168fEhISsGnTJuTk5HiEmOLiYvTr1w9btmxBQUEBfvnlF9xyyy1ISkpCx44dMWTIEOzdu1d3+5988kmf99dddx06dOgAAOjXrx9KSkoAAIMGDcIXX3wBANi+fTs6dOjg2RcnnHACVqxYEWBP+5OZmYnGxkYAQFpammd5AHDyySfj9ddf1/zur7/+qrovDh48SMIHQRAEQRBEHHPZZZf1stvtnpK7DoeD9e/fv0E5X0JCgrRjx46UQ4cOJRQXFzsmTpxYr7XMd999N//WW2890r9//xYA+Pvf/75/xIgRA1pbWz3Cx1/+8peDWVlZLuV39+7dm7hs2bLsI0eOrOvQoYMTAM4555w6ACgqKnLOmDGjis/7pz/96dCZZ57ZJ8hNjwlI+CCINkhaWhoaGhpU/TXq6uqQluYVwLOzsz2vGWPIyspCbW0tunXrhjPPPBNLlixBWVkZevbsiUmTJnmiP959910wxny+W1dX53kvdtSTk5PRu3dvbNq0CWPHjsXmzZsxefJkAEB1dTX279+Pxx9/3DO/y+XC4MGDPdsgLktsrxb33XefpsdHRkaG53ViYiJaWloAALW1tX77wsi6lNTU1CA1NRWAnEu5aNEi7Ny50yOGtLS0aPqeaO0LLtQQBEEQBEEQgQlHJEaovPXWWzvUzE2V87366qt7HnrooU79+/cfUFJS0vLwww8fvPTSS6vVlnnkyJHE0tLSFv6+d+/eLU6nk+3fv98T2aGVnrJr167E7OxsBxc9RGpra2033XRTlyVLlmTV1NQkAEB9fb3N4XB4jP3jjfhsNUEQunTu3BkJCQn45ZdfPF4ZgNzp3r59OyZOnOiZVl3tvY9KkoSamhqPMeegQYMwaNAgNDc349NPP8XixYtx/vnnIzs7G+eddx66du3qt+6qqioA8BFFAGDgwIFYunQpunXrhtbWVnTv3h2ALJCUlpbiyiuv9FsWFwhqampQUFDg114rycjI8NsXZtclSRJ+/fVX9OjRA4BsMHvs2DFcf/31yMjIwOHDh/Hf//7XM79yH2nti3Xr1pncGoIgCIIgCCIeGTRoUPMnn3yy2+l04o033sidMWNGz3POOWed8rkRAAoLC1v37NmTxN/v2LEjyW63S507d27dtWtXEgDYbDbJ74sAevTo0VpdXZ1QXl5uLygo8BE/Hn300cIdO3ak/Pjjj7907drV8cMPP6SedNJJ/SVJdVFxQWRLLRAEERFSUlIwbtw4LFy4EDt27IDT6URVVRXef/99ZGVl+fhWHDp0CL/88gtcLhd+/PFH2O12dO7cGeXl5di9eze4spuQkODpqI8YMQLffPONR+Sor6/H1q1bddvUu3dvVFdXY8mSJRgwYIBnWccddxyOHTuG9evXw+l0wul04sCBAygrK4PNZkO/fv2wZMkStLa2oqysDOvXrw/LPjvuuONQVlbm2Rc//fSTTwQLIEdzNDU1+X3X6XSirKwMH3zwAerq6nDiiScCkIWmxMREpKSkoLGxEUuXLvX5Xnp6OiorK33aoLYvamtrQRAEQRAEQbR9/vOf/+QdPHgwwW63Izc31wHI6S/FxcUOm82GX375JZnPO3369IoXXnihcOvWrUnV1dW2++67r+Scc86pNFK9pVu3bq2nnHJK9TXXXNO1rKzM3tzczBYuXJgBALW1tfaUlBRXQUGB88iRI/aZM2d2CtsGRwiK+CCINspJJ52E1NRUfPnll6isrERycjL69OmDCy64wCdErU+fPti8eTPmz5+PvLw8XHzxxbDb7XA6nVi8eDHKy8ths9nQpUsXnHvuuQBk/wsAmDt3Lmpra5Geno4BAwagb9++mu1JSEhA3759sW7dOkyYMMEzPTk5GVdccQW+/PJLfPnll5AkCYWFhZg0aRIA4KyzzsLHH3+Mf/zjHygoKMDQoUOxZ88e3W0XU0UA4NRTT/WIEVqkpaVh+vTpWLhwIT7++GMMHjzYL6Ll6aefRmZmJu6++24AwObNmz2CT2ZmJnr06OFTyvaEE07ABx98gCeffBKZmZk48cQTfQSi448/HvPnz/eYqJ511lmq+6K0tFS37QRBEARBEETb4Isvvsj+4x//2KWpqcnWqVOnlldeeWVXWlqaBEC64447Do0bN66vw+Fg8+fP337nnXeWHzx4MHH8+PF9m5ub2SmnnFLzyiuv/GZ0Xe++++7uW265pUvfvn0Htra2shNOOKH2rLPOqnvggQeOXHTRRT0KCgqGduzYsfW22247vHjx4pzwbXX4YfEcrjJy5Ehp1apVli6zvLzcE1JPEMFA51DbZdasWQCAmTNnRnS9PNVl6NChYV0PnbtEKND5Q8QrdO4SoULnUHhhjK2WJGmk3jzr16/fM2TIkPJItYmITdavX18wZMiQUrXPKNWFIAiCIAiCIAiCIIg2CwkfBEEQBEEQBEEQBEG0WUj4IAiCMInL5VcKnSAIgiAIgiCIGIWED4IgCJM4HI5oN4EgCIIgCIIgCIOQ8EEQBGEA0Qg6UOlegiAIgiAIgiBiBxI+CIIgDCCmtwQqp0sQ7ZamJqCyMtqtIAiCIAiC8IGED4IgCAOIwkc8lwEniLDy0kvAs89GuxUEQRAEQRA+kPBBEARhgO3bt3teU8QHQWhQURHtFhAEQRAEQfhBwgdBEIQBGhoaPK+rqqqi1xCCIAiCIAiCMMndd9/dacqUKd2j3Y5oQcIHQbRRJEnC559/jlmzZulGKDQ3N+Ozzz7DrFmzUEm5+ZqQoSlBmIBKPhME0d7ZtQvYti3arSBijNGjR/fJysoa2tjYyALN+9xzz+WPGDGiTyTa1R4g4YMgNFixYgXmzJkT7WYEzaOPPoqff/4ZAPD666+jtbXVb56WlhY8/vjjWLVqFQDgueeei2gb44ndu3dHuwkEET80N0e7BQRBENHljTeAt9+OdiuIGGLbtm1Jq1evzmCM4e23386JdnvaGyR8EIQGX375JXbt2mXpMtesWYN//etfli5Tjerqar9pf/3rX9EsdEZaWlrwt7/9LextaSu4FCPYZHBKEArEa4JGOQmCIGToeYFw8/LLL+cPGTKkfvr06eVz5szJ59N37NiReMYZZ/TMzc0dkpOTM/Sqq67qumbNmpR7772327p16zLS0tKGZWZmDgXkiJGnnnqqgH9XGRVyzTXXdCkqKhqckZExbMCAAf0WLVqUEdGNjGESot0AgohFRD8HzpYtW9CjRw+kpKQEvdxPPvnE1PzNzc1ISEiA3W7XnKeurg7//Oc/kZqaitzcXDidThw5ckR13scffxz33HMPdu7ciY8++shUW9o7HTt2xNGjRz3va2pqkJ2dHcUWEURswcrKvG+czug1hCAIIpZwOoEE6nJFnHnzsnDwYGJY19GpUysuvLDG6Ozvvfde/q233nrkpJNOqj/11FP77tu3L6G4uNhxzjnn9B47dmztvHnzNiYkJEjLly9PHz58eNPf//73va+//nrB6tWrDY8mjBo1qv5vf/vbwfz8fOdjjz1WeMUVV/T87bffNqSlpbV7BY4iPoi4o6ysLOxVNQ4dOuTzfsOGDXj//ffxxBNPWGJsaTRa4PHHH8drr72mO8/8+fMBAI2NjTh48KCm6MH5xz/+oSt6zJo1y1Db2hui6AHA8migmKa6GnjkEWDTpmi3hDDLkiVAhERO+/793jcOR0TWSRCESVQGdtosLhfw5JPAmjXRbcdbb0V3/URM8MUXX2QcPHgw6eqrr648+eSTG7p06dI8e/bsvCVLlqQfPXo08cUXX9yXlZXlSktLkyZNmlQX7HpuvfXWiqKiImdiYiJmzZp1pKWlha1fvz74Uds2BMmPRNzxn//8BwAwc+bMsK1j7ty5ntdvv/02fv31V8/7Z599FnfddReysrJ8vvPRRx9hw4YNuO222wIuX5IkMBbQ0wgAsF/sTKjQ1NRkaDlm2LNnD0pLSy1fbjyxe/dudOzYEenp6aqfL1iwAMOGDYtwq6LE2rXy/3nzgIEDo9sWwhxLlsj/zz8//OsSxY4DB8K/PoIgzLF+vSyE3nQTUFwc7daEH6dTFnoWLACGD49eO9rTQEksYSISIxK8+uqr+WPHjq0pLi52AMC0adMq3n777YKSkpLWkpKSlsREa4JTZs6cWThnzpyCsrKyRACor6+3Hz16lPr8oIgPIo5Rei6EC1H04Dz99NN+URsbNmwwvEwjER9Go0LCsR9ef/11y5cZb7zxxht49dVXo92M2GDLFu9rqtZBaGATRVoK6yaI2OPbb+X/ASJJCYKwlrq6OvbZZ5/lrly5MrOgoGBIQUHBkJdeeqlw27ZtqcXFxa0HDx5MUitCoDZImpaW5mxoaPD04Q8fPuxRTBYtWpTx/PPPF73zzju7qqqq1tXW1q7LyMhwki+dDAkfRNzS0tIS1fW/9NJLQX/XyA3IYTBUPFw3s99++y0sy40H+D49duxYlFsSI4hpPv/4R/TaQegjSXJK0iOPAAcPhnddmzYBH3/sMylhxw7vm969w7t+gohXnE6gJkoD0TxVt71UXRJNllVM32OGqiogQJoyEd+8+eabuTabDevXr9+8Zs2azWvWrNm8adOmTSNGjKj78MMPczt06NB62223da6pqbE1NDSwL7/8Mh0AiouLWw8fPpzU1NTkUUAGDRrUuGDBgtza2lrbpk2bkt98802P0Wl1dbU9ISFBKioqam1tbWX33HNPcX19vbZRYDuDhA8ibtkhPmRHgcOHD6tONyJEGInS4CVmA2GzaV/GgwcPxsMPPxxwGRdddJHftPYc7aAn+ihTnNod7Sk/PN547z3v6xCEWUPMmyenQGnlzlNkENEe+Ppr4MsvzX3n/feBp54KvzgZiEceARR+ZhGnvBz46Sfrlvfaa/J28eew8nLvZ4sXB/5+WZmcHqj2HLdzp7zsl18O7v6m92z4zDPACy+YXyYRN8ydOzf/oosuKu/du3dL165dHfzv5ptvPvrxxx/nffrppzt27dqV3LVr18ElJSWD33rrrTwAmDx5cm3v3r0bCwsLh+Tm5g4BgIceeuhIYmKiq6ioaMhVV13Vfdq0aRV8PdOmTaseP358df/+/Qd16dJlUEpKiquoqCi6I8UxRNhiURljswFMBnBUkqSB7ml5AN4FUApgD4CLJEmqdH/2IIDrADgB/E6SpC/C1TYifhGjII4cOYKBYfAbqKsz7id04MABlJSU+EwzImoYEUe+NPgwVV9fr/nZ1KlTwRjD5MmT8emnn6rO07dvX/Tr1w+TJk3CF1/QZQf4VvVxOBxIEML2+/Tpg59//jkazSIIfX75JfLr1Mqdp7Baoj2wfLn8/4wzjH9n61b5/1dfAVdfbX2bzPDf/8qd+Wjxr3/J//fuBVQGYExRUwNw4/tZs4CZM70eRwCwcSMwbZr+Mv79b/n/iScCycm+n82ZI/8/cAB49FHz+83lAtQq9JERdLtg+fLl29WmX3/99ZXXX399JQAsXrx4p/LzlJQUacmSJT4jvcXFxY7vv/9edXkJCQl477339gLYy6c99thjnnCip556KsqKa3QJZ8THawDOVEx7AMDXkiT1BvC1+z0YY/0BXAJggPs7/2GMUVgO4YcoKhhNBTHL9u2q9xJVXnnlFb9pTgNlHK1MT6nWCN986KGHPLmBI0aMwB//+Ee/eWbMmIGLL74YAHDCCSdY1qZ454BgzPjbb7/5vD/++OM9r6OdbkUQunz2WfTW3dgYvXUTRDywe3e0WxA7iD5SwbJ+ve97s6ktZoXjH380N7+KfwMAqpZGEBEkbBEfkiQtY4yVKiZPATDe/fp1AEsA3O+e/o4kSc0AdjPGdgAYDWBFuNpHxCeiYGBEYAiGH03+mLW2tkJ0Yubt+v7777F48WLMmDED3bp18/lOoKiQmiDyf6+44gqUlpaipqYGOTk5foZIdrsd9913H1pbW8EYQ2pqqk8kAwBMmzYNH3zwgU87lak0vNztww8/7Pf9toIY8SFJEhqFTlx2drbn9dGjR9G5c+eIti3iqIl0Bw8CnTpFvi2EOcTIJEkCDFaSsoTPPgNGjYrc+ggimlhxff32GzB7dmQrrhQUBJ4nXlCUnMczz8hVyIwKC+++633d2uob8cG9UUQWLZL/3KQkJADiAJPyt/Pxx33f3367vP/nzzfWPoIgQibSHh+FkiQdAgD3/47u6SUA9gnz7XdPIwgfROEjIyMjLOs4qvzxBJCWloaZM2fi1ltv9fvsr3/9Kw4KubpNTU1YsWIFFrvzSV977TVIkuTT9kARH0rhQ29+XlK1R48esNvtyM3N1SyVm5qaiqysLGRmZqqKFsrUIeW+aBYM0dSq3bQVlPt7iRAuK+63cEUdxRR79/pPe+klSmXQY8sWOQzaRNpc2AlD2WtdUlIiuz6CiCbBGmeKnkmzZ8v///vf0NujhbIEu+iBEW1C9QXauNF/WrA+KkqxxIDRuU05YBVoe3iaD0EQESNWhmvVemmqT9WMsRsB3AgAnTt3RrnFN22ttAEiNhA73hs2bED//v0jst6rr74a5eXlYIyhpKTEJ/UBAF5++WXP67feesvv+48++ijy8/M978vLy32iCJTU1tb6vD969CjsarmhkKMykpKSwlKBZOXKlRgzZozn/XuCeWJNTY3l11+ssG7dOs/rQ4cO+RxvcZsPHDgQNgFOhJ8P4d7favc/+/79SFYxNG3+6Sc4e/UKa3vilTR3qUjHxx+jZdKkyK1Xx3i2obwcSE0Ny7oa3OelTRBXWoYNg6ON3h+ItkdQz35Op+c6aNq2Da6ePQ19Tbx2nLNno/mSS/ymN69YAWdJCZCWZr5deuv+/nu/aQ3Ruk4lyWebHW+/HdL9UvX+p5jWsH+/qijLamqQKszbUlbmc/9K2LwZSQGMvZuamtD844/e38XWVt17MgC4nngCNmGexh07IOXk6H6HIIjgibTwcYQxVixJ0iHGWDEAPpy8H0AXYb7OAFRlWkmSXgLwEgCMHDlSKghDmF44lklYgygWHDt2zPJjpUxB6d+/P0455RR07NjRM+3666/H3/72N9P+DqIwkZubq1sdRJnGk5eX55NOI5KQkIDExETL9sXtt9+Of7lHIjIzM32WW1ZW5nmt/Kyt8vXXX/u8F7d58eLFOOmkk8LehszMTL91hwu/dSxerPrwnbZoUXRN8WIZvr/S0iIbSq7TSUorKLBU+BDXlebexoaUFKSlpQE2G9IyM9tWGD3R5jF9f21u9lwHaQsXGr8fitdpeTkyCwpkfwnxmuJG47/7HZCXZ65dRtfNJ0XrOnU4fNuzfTtw+eXBL8+ASJT2yitAURFw882+H3zxhe/+z8jwvX+tWWNs+V9+CXC/NOH80KSx0Xe9DQ0ADSgQRNiIdKrLAgDcwvpqAB8L0y9hjCUzxroD6A1gZYTbRsQBzWGuPS+mrJx33nmYPn06CgsL/eZ78MEHQ1pPoFSX3QrTM+V7EWXVkVARI1OWLVumOZ+R6jXhRJIkLFiwwC/6xgqGKcOB3XTo0MHydRFtDDHcWafiUli58srwLl+n3DMAICGBKhUQbR8rfcZEfwmR556ztjR0LImRavsvEs8Vhw/7+4EoTe31ntH00vjE9vPXZnzAolGZiyDaEWETPhhjb0M2J+3DGNvPGLsOwOMATmeMbQdwuvs9JEnaDOA9AFsALAJwmyRJ4XGuJOIavdKtVvC///3P81qr82sFgYQPZdpKZWWl5rxWCx8AfCJcOLt27fJ5v3r1auzc6Vd5K2I0NTVh7dq1mDt3ruXLrlIzMgMiEt0R00yZEu0WxD7iw/z+/ZFbr/jArWaMaKUny5Ej2p8VFZHwQbQPghE+hKhJD4cP63/HygpJsZR+pnaPWLo0uGWJg0OjRweef98+/c9zc7U/u+ceY23i9+QBA4zND+jfWwmCCJmwCR+SJF0qSVKxJEmJkiR1liTpf5IkHZMkaaIkSb3d/yuE+f8iSVJPSZL6SJK0MFztIuIbMeJDrXMeSU477bSgvxsoWkJZKURPKAmH8HHKKaf4Tdu8ebPP+71794ZFdDAK3ydaRq5mlvO///0P27Zt80zTirDp5K5komZy2y4IoxjYZnjnHe/roqLIrVfsRKiFVx86ZF0HSq1MLvclOnxYzqtfSUGbRBsnGOFDqALi4cUX9b8TRJW3uECsOsUJVvgQn0XGjw88v5oAJfKxOyD9uefkNBjOsGGysGsEPoBixj8mTNUKfTh8WP494HAzbvI4JNoBkU51IYiQEH011KqvRJIhQ4YE/d1AER9K/w+96iEVFRWWVxcR188rzKxZs8bSdajhdDpRVVWFLVu2oFWr5r0bLh6FKny0tLRg//79eEfssGrAj1uuMBpk1uslbjnvPPn/2Wd7p9GDkj/iiN2ePdYsU5L0IzZ27QIWLNBfxpw5wBNPWNMeNajKD9HeUHZSjdwPg4mSXLvW/He00DBJjwpWRsSJxyItDXjgAd/P77zT9/2PPxpbbkUFsGKF9z031P/Tn4Bu3dS/w59d+GDR6tWyd0lSEhAoalTDy81SXnzRt3IQN62naBMCwN13391pypQp3aPdjnARK1VdCMIQsdTJNFLN45RTTlH1yQgkfCgjQn744QeMHTtWdd6yQCMXQdCli9dr+KOPPsLVV1+tOa/T6dSsOGOGmpoaPP30037T//SnP8Fm89do+T5qCOCaHggzXiVJSUkA4LO9ra2tnukA8MYbb+DQoUO4//77Q2pXzMHTJ0SjvaYmIDs7Ou1pT8ydK3eYZs4ElELfhg3Ahx/6fycx0fsAHm7WrgVKSyOzLoKIFZTCR7hG6xVppkHzwguRiSgwilZb9u0DunRR/8woog9HYaF+6ooZuPGo3Q5cc413+oYN3qiTHTuAfv28qS42G9C7N/DQQ/L700/3N8Lt0wfYts1Ymk64CHEQiTDO6NGj+2zdujX1yJEj61NTU2nUIIJQxAcRV4QqfOzbtw+zZs3SNQsFgAEGczJvvPFGz+tzzjnH57N7770Xp556Ki677DK/7wXqbPPPubFqSUmJofaEgz179mDLli2an/9ikRmXVjneiooK1elWmasqK+jokeMuMydGmTQJJTwBOU1GOa1NwIUPsWSjjvltu8TKXHwRPkqsSDcDoC56AOYM9ULl44+9Yd0E0V5QRlpa0XF8+GHgppt8p/XpE/pygdgb0efPc0rRVPBas4TkZOPzimKGGlrHuGtX72seIcIHx9Se3/78Z+Cqq7zvhRK4ESOS6yI8bNu2LWn16tUZjDG8/fbbOdFuT3uDhA8irghV+Jg9ezYA4P3339edb8KECYaWVyyYCI4YMcLzeubMmXJZRwC9e/f2mQ8IHPHBP+deG/369TPUnnCht7+OWPQwpZWuoxbtAZgTLPSoq6sL6fsffPCBJe2ISdRysMUHP7WOeHsmgKAaFGKY9bx5xr8XggdRUPCqCJEUXAgimih/g8wI3mec4e8Vcc89cqRWcbFvRMD33wfdREOEGDUZNLwimyQBI0das0y1VBFehWrwYO3v2e3AkCHB37/cgyIAvAI4rwKn9jxpswE9enjf87Z99VVw6w+GcAh3cczcDcgb9QoGlT6LEaNewaC5G2BhHWkvL7/8cv6QIUPqp0+fXj5nzhxPGcVp06aVXnnllV3Hjx/fKz09fdjgwYP7bt682aPaffXVV+kDBw7sl5mZOXTgwIH9vvrqq3T+2ejRo/v87ne/6zRs2LC+aWlpwyZMmNDr8OHD9vPOO697RkbGsIEDB/bbtm2bJzT5mmuu6VJUVDQ4IyNj2IABA/otWrRINYR9/Pjxvf7yl7/4GCoed9xx/efMmZNj6U6JICR8EHHFV+4fBcYYigTjwMbGRtTU1GD9+vX46aefAi6nUWVkdtOmTZ7XiSbyLGfOnImZM2eCMYbzzjsPZ555pt88N9xwA6688kpceOGFAIxHfPB26M2flpaGkVY9NBjg5ptv9nkfaqoJ5+uvv1adruXhYVXEx+rVq33e65XwVeOQaBIWr6xbp272pmZiSWhjRpgwitn8fj5abFUuf2urMe8Cfh+IZqg2QUQSwWwdgLlIhRNOAG67zXeaskzq5MnBtSsQqam+USXR9mrq2RM46yzfad98YzyCTmy/+xlLlQ0bfN+7XN7OP2NAZqb3vtm/f/C+RXyZ/BlF73ny/vuBu+7yFcF275Z/k4NZv0bkrAe9ZfKok3bI3A3Ie3QZuh2tR5IE4Gg9kh5dhm7hED/ee++9/IsuuujY1VdfXbF8+fKsffv2eQ7+ggUL8mbOnHmwqqpqbWlpafP9999fAgBHjhyxT5s2rfctt9xypKKiYt0dd9xxZNq0ab0PHz7s+aGfP39+3ty5c3fv27dvw969e5NPOOGEftdee215ZWXl2t69ezc+/PDDnfi8o0aNql+3bt3mysrKtRdeeGHFFVdc0bOhocHvYfuKK6449u6773r2wYoVK1KPHj2aNH369Lg1eCPhg4hLOnXq5PFZOHz4MJ588kk8/fTTmD9/PhYtWoQPVcK/9cwry8rKfEbujfh3qDFs2DD0FFMB3DDG0KNHD0/1FaMeH3wb9Tr5LpdLMyoiFNLUKkMAyMvz/R2wyvRUq4Ss1rZbFfFRy6tRuPn2229NfT+aaUiW4HQC8+cD//43mE7ZZEvZvDmwq36k2L3b6+AfKmrnaqjnqdLEOVBKCTcmtmr07o03ABXvHT/WrZP/R+ocIohI09ws3yt5ZIcywsOMybjN5u87oRQre/c23URDNDbKUSU8kjTaI/0DBvhv+7Jl/mbMlZXq9z/x/qSXFsSNSTkvvQQ89pj82uWSjwlny5bg0/cqK4H6eu+9X0+ETk2VfbLEdb/+unyemRHSNm6Uo4Sef94bfaeGKCbt2uX/+9JOeXYlSpqdvn3iZidsz66EpQ94X3zxRcbBgweTrr766sqTTz65oUuXLs2zZ8/2PFSfeeaZlaeeempDYmIiLr/88orNmzenAsC8efOyu3Xr1nzbbbdVJCYm4qabbqro0aNH03vvvZfDv3vppZeWDxgwoDk/P985YcKE6q5duzZPnTq1NjExEdOnT6/ctGmT56H+1ltvrSgqKnImJiZi1qxZR1paWtj69esVyitw+eWXV+3duzdl48aNyQDw6quv5k+ePLkiJSUlbn1JSPgg4pIDBw7ggDtM8r+iO7WbjRs3+k0Ty5WKSJKE//znPz7TQq0UogVfrlHhgwslesJHU1NTWNo7ceJE1elmomHM0KwcPXOjte3i9ED7Uw/xvFAuR5miJHLXXXcBAA4ePBj0umMCoWOe+vLLxr4jVnYJhvffB/79b+/7DRvkh7ZoVAZ5/XU5quLw4fAsX+O8NoSav81LL+l/hxvtWiWG7ttnbn7B6Jcg2hQ//igLfDz9bOHC0JfJS4Tffbe/ABGGAQ0AwPDh8n8+wq9WYjeSaAyyAACefBKoq5Mjyp59FnjmmeDXoywvzu/5b77pL3wA/qK1mfTBzz7zft/IcVR7hjNT9UZMuTUahfr++4Di2be9UlYP1R8urenB8uqrr+aPHTu2pri42AEA06ZNq3j77bcL+OeFhYUe45X09HRXQ0ODHQAOHjyY1LlzZ5+Hic6dO7ccOHAgUfiuR3lNTU11dejQwbOstLQ0V0NDg+dEnDlzZmGPHj0GZGZmDs3MzBxaV1dnP3r0qF/Bk9TUVOmcc86pmD17dr7T6cT8+fPzZsyYESCsKLYh4YNos6xcuVLzs4EDB3peP/roo5FoDgCvX0WgNA3eAQ8UIcLFAiPpPWYZxh/IBIqUDw4RwEjER6DSt8Gu6/zzz9eclwtAWscmFDEmoih9OtQ66koBKD3df55Q4BFaVlUvCIaPPrJuWWLnPxQPmeee85/W0OAViJTRFaNGecOqrRZDuZePKMYoS0YCvtv+00/qUTCxztGj0T0XidiEn8v82qqvl/+HIlBMmSKLvooS9j7rsQLx92jcOPk/T0+zqux2sHDz0Tvu8P+soUGulvLPfwZejuizAQCdOvm+1/pN5hESyuOovHeNGaO7eklMVXI4jEV8RJp4vB9HgA7pUDUQ1JoeDHV1deyzzz7LXblyZWZBQcGQgoKCIS+99FLhtm3bUlesWJGq991OnTq17N+/38el98CBA0klJSWmH34XLVqU8fzzzxe98847u6qqqtbV1tauy8jIcGo9s1577bXHPvjgg7wFCxZkpaamuk477bR6s+uMJUj4IOKKLiZKnC3UGY3ZtGkTXnnlFcyaNcuKZhnGbMQHT3XRMnUN1ZhTD7UoErGKTbg477zzcNNNN3kqqGgJH2KkhVXChzJ9Ri+6JVAJ36OxEEJqxAx4wQLf92qjTEpztr59va+tFHiiWQ3Hyjx30efn11/l/xUV1u2rWbPkztKzz3qn3XMPIFaWUus0ZWYGv05+XogVnlJS/KsgiMLHwoXe0WSnE1iyxLeSwMKFwKuvBt+mcPGf/8gpPgQhwq9f3kHmEQDi9c6NNNUwGx2oFwlhlvJy72tegjzaKS4cvj+1SqMfPmwsZVCRhot8t28k988I1OlXCh/K3/8AApeUqui7WiF8BCNK6W0nCR+q3DkaB5Lt8Nk5yXa47hyNA1at480338y12WxYv3795jVr1mxes2bN5k2bNm0aMWJE3ezZs/P1vjtt2rTqPXv2JL/44ot5ra2tePnll3N37NiREozXRnV1tT0hIUEqKipqbW1tZffcc09xfX295kl62mmn1dtsNtx///2dp0+fHtfRHgAJH3FBa2srvvnmG8s8DeKZ1NRUFBUVeaqdhDKqzlNllMyYMSPoZQYi2FSXJUuWeD47dOgQvvvuOwDalVDCRbhSgABvNEy/fv1QVFSEs9xmZ1rCh+jD8pFFo/XlwsPh2Wef7bOOM844Q7W9Wvym9wAcTvbtkzvFixYBf/2rPHp99Kg8Ta1Ki/JcnDPHfx7ltorvrXyQcjiAmhrgk0+AL7+UDe4iVTLXStHluOO8r5ub5Q7Pc8/JERBGMXu/V/oSqZ2foaShfPKJ/H/xYt/pyuifxETfCg088m7NGln4EFMTf/oJ2Ls3+DYRhJVIknydalUq4/dK/rvLO8bidTV7tna1K7OdWCtTXdTSGMXf81iIUFRWudFC6zdHub+4MHXrrfL/QNsY4v6WxHvwr796z6NQhI/XXlM3LOWG5B9+6H9PFp4X/SDhQ5UrBqPiz6dgb8d0tDAAHdPR8udTsPeKwVDJNw2OuXPn5l900UXlvXv3bunatauD/918881HP/roo3yHw6H5gF1UVOScN2/ejueff74wLy9v6DPPPFM0b968HTxlxgzTpk2rHj9+fHX//v0HdenSZVBKSoqrqKhId5TsoosuOrZ9+/bUa6+9Nu6FD4N3GSKaLF++HMuXL0dGRgZGt3PH/L1794Ix5ul0hiOdoFu3bpYvk2M01UUZ8SHykjvHf+zYsarVaeKVQYMGYc+ePUhRONtrbaN47Hfu3GlJG8TSvKmpqR7hCQCG87xoN+KxmTVrFk4++WSfMsiff/45Ro0aZUm7TMEjDH78Uf5/9KhX8PjsM/kh6cEHvfPbbIEfhpQhw+ID85YtwKBBvp+vXAl8/jnwpz/5P/TpXbMOhxyBsmOH73S30Gk5FkUK+SE+AC9f7i1ruG+fXM3BCKFGoKiJlOHo3Cg7K3l56pVouImwOPIcDaqq5H2jNbocazQ1yR1mMcqKCA9r18qRWS+84FtOlsPFyGXL5Cg4LsoqTUrff19OD1RGIIRyv/ngA2DiRP90DqOoRf/17+8tn7pnD9C9e7Cts44TT/Qt4a3Grl1y+stNN/mmYSoHgrKzfY+jWeHD5HNF67hx3rRNAFi1Sv4faqpLfb03eqWqSk7NCbbimpagTmXIccVgVFgpdChZvny5quvs9ddfX3n99df7uYJPnjy59siRI55SRJMmTarbvHnzL2rLWLlypY+J4XPPPecTXjZ16tTaqVOnbgLkAdX33ntvLwDPqMNjjz3mefh96qmn/ELTunbt2jJ8+PC6/v37W5b6Ey0o4iMO4NUuDofLfC+OaG5uRlNTk2EBwaoUCKsINtVFa5733nsPADBp0iSLWujLddddp/v5cccdZ5nvR3NzM5KTvSmMW9wh9YuVoxluxCo8VtGBd1Dhb2yqTHtRRr98//33lrcnKJTXREWF76i60sNDTVRQdk6VIbwiaulWn38u/1eLehHPfeV14HD4ix7hJFL3VB7pwUUpIyj9PQYMMLdOtdHLSAgfHTqoC2l6kTuKykpBIUlyZ0jNm6OiQu4ALVggmyMaqVITK8ybB7zzjhwJRYSXQOehUoTnHh9qJtjPPed/vZm5/pVs3BjY2LOhQT7PNYzc/RDFv2hEFOfl+YvmJ58c+Htz58r/d+70FZMCRe0ZFT54Zb5PPw3cFgGXmk8LYFz40Bp0E0WrN94Ircy81jNzmEzrifintrbW9vLLL3e45pprojxqYQ0kfMQBW7duBQCsVRtFa6eoCQg33XQTHn74YZ/5uPChVuVFjSuvvNKiFqpjVPjgn3Ph4zgxdN6Ny+XyREOk6nVMQyA9gIml3W63LAXL6XT6CD1c/NHyNwkGSZLw0UcfYb+GW7pYWSbfPcLyxz/+EXfffXfA1JZAIlzEUJ5b33zjP0+gkr3vvmt8fXqRCWru8uJ+mjUL+L//876P9Ci81UJAfj7AjZN5xYQuXbwhz8EKsRMmABdeqP35uef6T1OL+LDyHOWdPeVDPWPGHqLF67pexyvN5TLWKWtqkkUztxjsww8/yP/Nlt7esiV6KQAtLfK2cyFQOZr92Wfe0XqHA9BI3SQM0NwsH2etjiunY0fva/Gc1OrYin44gPeaVCl5bwm8RLhREV68R8ydG74IOCVbt8qpcxUV/gJtWhpw553qYpKSH38E/vIX73tu1qqFUeEj2AhSrfPAaArNNdfIwtUf/+g7XWy32XLhCxfKy3zkEbk8rlhNTUQZnUQQAD744IOsjh07DikoKGi96aab4j7NBSDhIy4I1OEKB7FYkUJsE69iUin8CBQVFSEhIQEzZ870TNuxYwe2bt2KD8XwQw1OOOEE9OjRw8IW+2M21YUxhvz8fFWTTVFwCFeJ2RwhrPZBIT1i+vTpGDp0KJqbm1HGH7YCsG3bNjTpjMgohY9sdyc422Bn2EjaT319PTZs2IB33nkHgP95ruYVYrfbkRmKKWSkMSLyLV3qfa2Wz27wmALwptSosWGD/zRlJ1Z8H+n7jtXrq6ryPuROniz/37fPv9NqlhNP1DciHDLEf5oR4eOtt+ROSDCUlMj/1fLy1YQYTna2HL0wf753mlaHq6EBePRRX3EsGMx0ZERT4vfek9MWIu1D0tgo+/OIFceUx/Pnn70d3M8+kz0c3NGhYeWXX7wh/G2B1lbgb39D4rffyma9eoj3C/Gc1HpGU0YfcS8QK0o+KysP1dV5jYL5Pd3lkgUxrXu08pxavTr0dgXi++/lCCa+rvXr/efJzQUKCvynK1FGGwaKiurXT//zUJ+1tYQPs95oynuqeN82+pu1cCHw/PO+vlJ6pclVqvgRxLRp02oaGxvXfv311zvD1c+INCR8xAHhNJRUY+nSpXj00UcjbpwZCLGTyquZ1AT4ofvoo4/wrsHR63Cli4iYTXWx2WyaURXbhHDWJCsepFQQzz1xHf3798eUKVOwS/FgJ0kS9qgYuFVXV+Odd97RTU/ZtWuXTyTGIHcIbJ8+fVTnV6aiNAQa7YFXKKt3jzArBah6vZFng1glknz00Uf4nKeMmMFo2gDf17+4U0bHjvX93IrRP7WReuUoqIjWaFa4ommsTB9obpa3l4s9oYgqyu/yB47evdXnVxMf1B7ilfvx11/lTohRxO9zs1+1h32lD4H4W1JdDTz1lO9DuNZvzZNPGm8b7/Sriatq59WmTf7TJEmu6CKyZYvcoYykKKd2H9ILl+fVQiJRFendd02nAMQ07vtc4qpVvpFuailTWvcLrWc0pZDAPdqM+vzooaw8JFbn4pGL330nC2K8shKg3/kX5wsXPEopEAajdE3RubO6dwuH3zO7dvX/TKzco4VRc1azBPP799NP6qaoWlhZQYggYhgSPiLEd9995zGlNEukhQ9eQSQmynEK8M5/3759caE77DuYDv/vfvc7v2l/+tOfQmucQcymujDGkJCQoCp8iKlPSkNQK/nDH/6AP/zhD6qfDVLk53766ad4/fXX8QmvAOFmlXuEcIcJ/wbubfPFF1+ofn5IkUaxNcDItcvlwuzZs32m8f1sZaSPEVHLSFrMhg0b8LNaFRarUD7YT5zo+z7Quo2UllaLHNETVL78Un16TU3oURNqWOkTo0zJCkWsEa930dvj4ov959UypVP73RA71EY78qKxrfh9fu8VBBaJp8Yp90Vdnb9HiSjQWSGy8XQWo8ybJ/83Opqq9MaJNHpt4yP8MfabHRdoPV8pyxn/3//pR7fdcEPgdfFrJRwdZDX/ELVUQ56Cxzn+eOvbooXaOWzWu8gKhg71vj7/fO9rfnyUZt5XXGFMrLIyOlscXU9IkH//wplOS6kuRDuBqrpEiK+//trwvK2trfjrX/8KALj22mvRo0cPbNYqjxYGUlJS0NTUpGusGQ14579r166eMqPNGg+jxx9/vCcdRiQrKwu5ubmedJiGhgakpKRELJ3IaKqLKHy0tLSomrSKkRUdxdxji8lQlskUSE9P9xGfNrlHUdesWYPJkyejrq4OmZmZnvK7ZjAram3ZsgUnnXSS5ucVFf5m3fw4dOnSxS96JVhE4UOSJD/h8v/cIdIPPfSQNSlKra1yRzOAH4sf33zjHYEE/DsAWiIE55JLgL//3dw6geBGzhcskIWaSy8FNCKAog4XZnhlH8Go10OgUGtArmggin2it0dCgnfEsqlJFki0jrtWh661VX6oNnocxPn++U/1edxtaiwvRzrgPzJuxJSRs2qV+YgCp1OuULFxo68PQyD4vjzuOOCyy/SjpdSEt5YW+VhNnGjtiKlapJSRTs+HHwKDB1vXjvaA0c5kIJ+ZkhK5Wtbf/hZ6m4KhSxffKCqtyIYRI3zfKzv54WTWLP9phYWRWz9HrMKTnS1XTNq61RtVpbx3RiHdHIWF3qjMN9+M/PoJoo1CER8xiOhVMHv2bJ9KE5EgK5DBV5TgwofdbveUGeWj/srOuVZ6RI4iBDstLS2iHipGIz5EysvLA/pohCvVJRDKNByx/OvPP/+Mp556yi/KQ23b1aaZNWw9yMO9NUhQGWXjwofaZ8Eiilp6x3n37t3WrPAvf5EFCCMGkMoHuvff93nbYqZsrHh8zIgZCxcan5fDRam33zb/3UjB9z9/qFYTDAMZ9u3a5St6ANoCRkqKvtgl3tduvtm/nUY7fGrzaaXcBIsYIaIlemi194MP5NF4nqYQTNQDHy3/17+051ETPv76VzmdQRHhFjJqKRVa2x9q2eP2jt59k6dDGb2/JSd7vX3U4MsxE8Vr9Lddz79BD2VVlXCh9QwTqHRtOBDvjXa799rSEj5CibrmXkhmCVfaDEG0c0j4iEGUkRY85YRXmQg3XHiJNYNTUfjg+4inN9QpTK60xIxAniDhxqzwEek0J7Nw4UOSJGzcuNHHZ4OnqLz55ps+KTFbVDwe1CJg9MQcI34eam1VwtuiFCEG8socQSBui15kj1YKD+B7fvymVhJWjd279Tuzf/oToKh6pDR9dJgJexavMTWD1PYG70DpPbDqdbIkyT+8PhTEe4d47vNzS7wH6UU6qLV5+3bj6zaCkXx+rXQYK70A9FJuli/X/oz75FiF2giveG2LniVz5li77vaGnik2T4cyYxo7cmRIzfFjxgzrlqUWlRSpgR+taiJa3mo6kaYhI1ZItNu9wie/rqyM+NATwvQwG8FJEIQhSPiIMEZKf36vKEXG0znCIXy0tLTg6NGj2LNnD8rKyuBwOFDrfgiOZeGDCx1KnweOVprOaaedFp7GGcRsqku8IEmSX+UccRt7CuX71AxE+bE99dRTPdP00kDE6+jkk0821Ea1ijI8XUiZBtM7yBHt/fv3G474UEu9UbYLAF7lTv2BWLRI29xwzBj5AS8hwX8EKjvbm/McrNAWKC2Gl+UUzf5KS4NbVzgYPTr0DgCPCNBLEdTzKVETPaZODb494rFkDODXlssl/xlJYQGA8nLz6+7b19z8Rkar1e6ZaqmOailGRggkiqtVnwgHWgbL4rODKFQFc3ysIM5+ozR54YXA8+hFqV17rf80Xo5UmVYSDIGqmpkxsLz+evXpatsQKbR+55UVW6xE/O1VqypmpfBhpCyvGpFMQSLaFQ888EDRxRdf3C3a7YgWFEsVYRwOR0DvjBWK0D/u7xCOzvDfdPJR1TrnTU1N+Oc//4nu3bvjsssus7w9evBUhrq6Or+UFSPccsstYfXCMEIwqS5Dhw7FTp1yjEVFRSG3K1iWu0dB9wYo99gihLJv2bIFo0ePxr59+5CZmYmcnByPkCFGeegJH2JFm969e3vaoYdadZ+SkhJs3LgRQ4YMwVKhxOsAg4ZrPXv29Dk2//vf/3w+1zvOJTohsEbMTwH4pgiUl2uPfosRLOPH+44oV1cD69Z5O9mjRwMrVxpbPydQKWGxLCfnhBN8RzP13PbDTWpq6MZxPCxeR9DSjfhQS30KxWdJ+bDOQ825L8uUKcEvOxDBig96qB0ftd+vYH1znnpK/3OlaGWmw2kGLd+ccBobBkNjY/upBKFmHMpRqwCSkABkZvqLQ8GkugSa14wxsFYas9o2WIlyP2Rne1O0tCI7zzzTWJWZrCyvaDlunPm2paTIpVzXrvXeO2LB46N7d+uW9dBD/vs5mr+37ZQvvvgi44EHHui8Y8eOFJvNhp49ezY9/fTTv40bN858CHMIPP7444f5623btiX17dt3UEtLy+pgPOeamprYHXfcUbJgwYK82tpae25uruPMM8+s+t///hdk7l34oYiPCGO4QyPAw/rNVMRQo7a2VtXwUwtlp62xsRFPPPEEHA4Htm/fHtB3wmp423ft2uXn/zBUdOmGeopItEUPwBvxYSbVxW63e84btfMnUilQavDtqAoQCiyWZeUiyezZs/Hss89CkiSfaB4OF0HUysOuFiIHxO2vqanBV199pbqfSlUiDLiAdtxxx/lMN2rsG6h0rd713r9/f83PDFfpUab8aJ1X4vUQyDNIOVpvJP1FI/IKgHaYuN7D5OmnB16nlfD9Y0UJWr1zQkv40BKOlNVRzKCM+FCmmH38cfDLDuS/E44UPaPHJlIh4s8/b/0y9Ua59Z4dxOMRzpK2osl6KOdme4CxyETFrFljfN5o+UYoz93ERGDUKPm11vVq1OOLVxc76yxZ1DdLcrI3rYaLA1Z6fASLlb5t0RBuCB8qKipsF154Ya+bb775aGVl5boDBw6s/+Mf/3gwJSUloqFzaoUSQuHhhx8uWrduXfpPP/30S11d3dpvvvlm27BhwywVcqxuM10NEUD0lTCS6qL0pyh3h7KGGvHx1FNPYdGiRTjmHqlyBCgPqey0KUPuA33favjJn5iYiEKFE7iyAxqr3hi8XWZSXUQDUbXzZ+zYsRa2MDhCOReWLVumKnwwxpCXl6cqWBw+LAvWOTk5SBNGHZ9++mn88MMPqhEo2YqQYbGsbLAGt3pVZAD9a1bZHpFKMYdfD2W7hRQZH8QIqUDRUsrSvmecEbgdoku+Ei2BVHmNig96Afar5fAou0CRK3rwe5KemZ3WdbJsmfr0ULwjlMJHMBEDWufvFVcE16ZQCDbiIVwh8+EwlNYTE8TtVz4IiudtOH2sxGhUo95Dsc6QIfqfqwlJRsqfWyV8KO+TSj8eo/5K0bhmOcprd8oU2dvjyiu1UzqMRrtxT5V+/YwLFFx0AWQR5pRT5PZwLzKrhI9Qojb0flNFeBWnm282X+GLYzY1kTDNpk2bUgDgpptuqkhISEBGRoZ0wQUX1Bx//PGem/czzzyT36NHjwFZWVlDx44d2/vXX39NAoDLLrus64033uhTt37ixIk9H3nkkUIA2LNnT+KkSZN65ubmDikpKRn02GOPeUZ577777k5nnnlmjylTpnTPyMgY9vzzzxfcfffdnaZMmdIdAMaPH98HALKzs4elpaUN++yzzzKys7OHrly50qM8HjhwICElJWX4wYMH/ZTTNWvWpJ977rmVpaWlrTabDX369Gm5/fbbPeGQO3bsSDzjjDN65ubmDsnJyRl61VVXdQXkvsx9991X3KlTp0F5eXlDzj///NJjx47ZATkKhTE24umnny4oLi4edOKJJ/bR2z9mIeEjAhw4cMDz2kjEh175UCvgnTFx1FwN3tbKykqsWrXKL8IjmOiVUODpEmqml8qOa6wLH2ZELJvN5tnXasJHNCM+OGYVWXE7lixZoip8AN5tb2pqUvXo0Fqv2v5NVzwUtLa2hix8BNr3etfIvHnzNM+DDz74wFgDlJ0ct9mvH8rRM15yVYvBg4GiIuDcc/UfQHnUijiSqNwmLbFX+WD30ENyWVAtszurEdvJvSLWrJFfB9Nh4d5MWvsrJ0d7X2j5R0S7pDg/f7t18/UBMFOp4KGHjM+rJ6AGU34XUPcBCYRW+qDY2dfqmBw8CPz0k68BqVGEFD4/xHNHz9zZaqPhw4eBDRvk17y8JuAb/RHPqD1vcY8OQD2lyUjkhNozSDCpLsp7t/L81nsOE0WdblFM6Vfe97p0kfeh4P3lB7/3Ka8z5fvu3eW0DTPVCMX9kpoqix8nnugdSLBC+LjhBuDqq81/j2O3+5uRqzFxorz9RUXAPfeop7DoPd/8+c9yafo2itMFVDfDFs4/p4Gu0MCBA5vsdjsuuOCC0vfeey+rrKzM58d9zpw5OU899VTxvHnzdh47dmzdmDFj6i6++OIeAHDllVdWLFiwII8/T5aVldm///777BkzZlQ4nU6cc845vQYNGtRw6NChDV999dW2F198sfCDDz7wXBCLFy/OufDCCyurq6vX3njjjT43tCVLlmwDgOrq6rUNDQ1rzznnnLpzzz234tVXX83j88yePTtvzJgxNZ06dfL7gR41alT9Cy+8UPj44493WLlyZar4zOtwOHDOOef07tKlS8vevXs3Hjx4cP3ll19eAQDPP/98/jvvvJO/ePHibbt3795YX19vv+6663xy7pYtW5axbdu2zUuWLPlVb/+YhYSPCBMo4kOSJN3KI1b4fPCIDz3fCADYsGED1q9fj+eeew6fffaZ6ueRhJeoHaIyQqPsMAdKQYgWRs1NRQJFfASTl2cVXd25wco29A0wgvCxIsz+hx9+AOBfrYULH0888QSeeOIJv+WoGaUC6ud2s6IDtGXLFixevNiznmAIJLAFul71PHZEfFKJHnnEW/Lzo48Mfd+PQGVrL7hAHkEKZM7HH6ZFAUh5br/zjvp3Cwr8p518svwAqodVgqu4HL4/Fi+WfSOWLDG/PG4yKd6LxJHhxETtjr1WR9aq0HTGgvNj4Odvr16yD8Ajj5jPDU9KCvw9nn5lNOJBD6PCmx5a/ievveZ9rRQYHA652s1LL8lmmM8+a15A00tTef1172s9YVSvQo9Z9u0DXnwR+PBDvwpQuiJNLFJVpS7WqF2T4nUnDFx5CJQuCIQv1UXZ3n37tL0wxIoiUXxOMOVDwtESfa0w/Qz0e2+F8GFlO202+f553XX+84j7Saudeu2P0UHCtkZeXp7rm2++2coYwx133FFaXFw8dMKECb327duXAAAvv/xyh7vuuuvw8OHDmxITE/G3v/3t0NatW1N//fXXpEmTJtUxxqQvvvgiAwDeeOON3KFDh9aVlpa2Ll26NL2ioiLhH//4x6GUlBSpf//+LVdeeWXZ22+/7REuhg4dWn/llVdW2e12ZGRkBLwpXXvttcc++uijfN7fePfdd/Mvu+wyVVOrv/71r4d+97vfHX7vvffyx44d26+oqGjw888/nw8AS5YsST969Gjiiy++uC8rK8uVlpYmTZo0qY4v89Zbbz3Sv3//luzsbNff//73/Z9++mmuOJj5l7/85WBWVpYrIyND0ts/Zo8FCR8RQOysBurwilEVk1XKYFkhfLzj7ohsD1CScP369Zg/f77m5yvNGiCGCBc31Mw8leVHMzIyDFf7iCTBlLNljHnmD3TMIs14d06tUpBRlhdWslFhwsmjj5RVempra3W9ZC699FLV6WrVfpRtdDqdHkEhXBFCgY6z0UgZXtLa8yC9apX830yZxXDAw2zFkF4jHVQ10cMoc+cG/10Rcd8rI2VCubeJ55K4nXa7sU74XXd5X48ZE3w7lG0yU6qYw4+lVTniQtUmH/g+U4t84X43Rr0rlPeLYIQPretW79yurwcELyMA+tVA1BAMloPGynx+0ax5zhz/Dv9f/mLdusLNiy8C77/vPz1QmqbymALGvSSMeC4Z4b775Og7QN08+ccf/acNGhQ73g7iNThsmLHv8A69ch9a8VsdKE3LCuEjHM8UVkYA8gqHbVz4sNuA7GS4wvlnN3iZDR8+vOmDDz7Yc+TIkQ0///zz5qNHjybeeuutXQDgwIEDSQ8//HCXzMzMoZmZmUNzcnKGSpLE9u7dm2iz2TBlypTKuXPn5gHAe++9l3fxxRdXAMCuXbuSysrKkvj3MjMzhz733HPFZWVlHvW2U6dOpgyZJkyYUJ+amur8/PPPM9euXZuyd+/e5Msuu6xKbd6EhAQ8+OCDZWvWrNlaUVGx9q677jr0+9//vnTNmjUpe/bsSSopKWlRG5w9cuRIYmlpqaddvXv3bnE6nWz//v2emXv27Ol5SNPbP2a2DSDhIyKIo89GPD443PwwQRh9MPP9UIm1kqrfffcdAPUIh1yVsOMJEyYgy0z4YwQwKnyIn9tsNs/7bTE2ysbPTeV5uV8MiTaBUhhsbGz0eNyokZeXpzp9t0qFDGUbjylCmJXimRUYiewxcp39wr0elEKHkfvBWWcFnue88wLPowZ/sBbboVfVhHPrrcGtDwB27Qr+uyI//yz/Z8zf28Os14c4v2hMK/rTJCQE7mQBvuUrraqawVjgKB81+DViVXTfuHHAbbd53/foIaca8OtEUcodgNc75auvtJervHbFa0qrPCxHbR8b+d1QhukfOeKf3rJyZfCj/jYbcOONvtOMnPtWRQnxKkUiymjL1tbgUnqigZZw5hbdG+68U45gM1Jmmz+D6EWWqkV8BHsupKV5TUznzjW2nO7dY1P40PjN9oO3Xe039OabgUsvBX73u+DaE6gSXiyYm4rr7d1b/s/3SUqK7L2Vna1uAnvhhYGrdo0dS5VdosiwYcOaLrvssvJt27alAkBxcXHLP//5z721tbXr+F9TU9Oa008/vR4ArrzyymOff/557q+//pq0YcOG9CuvvLISAEpLS1tKSkqaxe/V19evXbp0qacaht7AntZnl1xyybE5c+bkzZ49O//ss8+uTEtLC3jTycjIkB588MGyrKwsx4YNG1JKS0tbDh48mKQ2wFdYWNi6Z88eT7TGjh07kux2u9S5c2fPzDabzbPOQPvHDDFyV2zbiJ4dgTpCoh8I97IQjSOPBJm/q5USAMgGkWrlO2NN+OBw4eOyyy5DXl4eJuj4FYwLprxZGAkm1UWM+NiqGJm+JMq5mXx7eKlhq5ZnFH5tBTIZBWThgzHm2Wc/CqNkjDFLzXp7ux9U1K6hBEXHxFTK2D6hQphR7wIjI/1GR+GU8OO1b5/8ELV+vX6Zz27dZBO6WHgg5+klSUnqD9f/+AfwySfyKPG//iVv3xtvAP/9r7/pqFgeWHyQ6N9fNhW86Sb/iA9Jkjv6eqUyrdxPjAEXX2zuO/z3JpjfnWuv9ZZIFhHP/wsvlDua/DpRixQbPlz+r9dZ0RPueHRU167AzJm+++D8830jbBiT58nIkPPrL71UjlJRMzLNyZErQvB1az1c8jLaGzfqVz9S8sADcrg8H+kH5PNPZNo0/++FGmJfUyOLHvPm+X+mFmH27LMAj0iLZ5KTgWuu8S2xrcellwLXX6/9udWpLuJovxHBe8CA4Drs4fBuE5dp1EiTd5ZqanxTKQH5XtCnj3ERRUkgg+9YKGfL13vnnfJ9EvCeAxkZcjTgXXepR4EMHAgoKh0S0WXt2rUpM2fOLNy5c2ciIJt+zps3L3/48OH1AHDjjTeWPfXUU8WrVq1KAYBjx47ZZ8+e7RnRPemkkxrz8vIcM2bM6DZ27NiagoICJwCMHz++PiMjw/nwww8X1dXVMYfDgZ9//jll6dKlhkZNiouLHTabDb/88otPDfrrr7/+2BdffJH7wQcf5M2YMUPzoe7RRx/t+Omnn2bW1dWx1tZWPP/88/n19fX20aNHN4wfP76+Q4cOrbfddlvnmpoaW0NDA/vyyy/TAWD69OkVL7zwQuHWrVuTqqurbffdd1/JOeecU6mVuh9o/5ghBp4+2z5iasahQ4d0ozaWL1/uea3WCVQbyTaCnm/IpEmTcOGFF/pVSlFLKQGAK6LpDg6vQtm7d2/ccccduiktVnXIrSLYVBet72hFPEQKnn60RVkqU4D7gBhBWZY4ELzsq/JaGaOSIuB0OmG32zX9X3hJWzPt1YILiUeOHMGsWbM8VWgAf4V9/vz5mKfWyRDwGPqKqQAG/UEMEeyIFt/vXAj46CPtkdVLL5U7FmY73+GCV6hwudQfbOvq5NHgzZu9/h27dsmd13ff9fUB0Ytw6tULKC6WH1AdDuC99+TllJfLUQxvveWdVyngWRXazI9vv37mRvlCiTDs2lX94VuMsLDZ1DuIYnlp7iOj56ugFCb+9z/glVfk17xSwxlnyOsSPRuGDJFH77kHycyZ3n2VmCh3rsaN848oAeRzo7nZK8hodRi3bpW374MPZNHMCNdd590mveoxfNtEQulsO53AU0+pix6AV0RS8p//BL9OszidwNNPh1bxiGNmXynNQfv08Y3OUqIX8RHM/Va8Joxcl8HeO8IRVSwuU2+fiYheNcoKVeEmlIiPG2/09VYJldxcb4QRH0RVGaj0o42nsMQbOTk5zp9//jn9xBNP7JeamjpszJgx/fr27dv4n//8Zx8AXHXVVVW///3vD1122WU9MjIyhg0YMGDAwoULfS6WCy64oGLFihVZl156qSesNiEhAZ999tmODRs2pJaWlg7Oy8sbev3115dWVlYaugFkZma67rjjjkPjxo3rm5mZOfTrr79OB+Q0kwEDBjQwxsB9OdRITU113X///Z2Li4uH5OXlDX3ppZc6vv766zv79+/fkpCQgE8//XTHrl27krt27Tq4pKRk8FtvvZUHAHfeeWf59OnTj40fP75vaWnpoOTkZOmVV17RzEEzsn+MEqWi3u0LUehYsGAB9u/fj3PFURwBsYylWghSsJVUWnRM4zp3lqskZWdn+0SUjB49GgsWLPCbv6eeE3cEMOPHEOmSu4EIJtVF7Tu5ubkYOXIkCkLxSrAAIxEaV111FR577DFcc801+PTTT3U9OzoEMI2rqalRTV9S7k+1dnHhQ83/IzExEUOHDkVBQYHnegiWoqIizzHjKSqbNm3yCIlq1/DmzZtxIR/VUcFzzmsZEqenBw7pV9Knj2xQKKZmmEXtWuT3jGHDgLVrvdPFB/dguOIK6/w9RPLz5VFIs4amS5bI1U10RD8fEhK8qQpa3+F51xyr6tcH+xAcjk6QeG2mpMjvlffDAQO8kTB6Ie9aiEIUT9ngAoKegaoWp5/uTTVQwvetXqWVt982t74uXbyv1ULZRS680FeoCEX4MHsPiQb19UB1tRyN1a+f+e9LkveY6Z3f55/vax59wgmASpl0TcLZ8dRq9+TJ8n3m229DEz6sNkLl7c3MNL5s8T4xcaI3csoKAh2bUISPTp2sMTZVIzUVePDB8JTSJsJK9+7dWz///HPdXMXbbrut4rbbbtPMFf773/9+6O9//7vfA2xpaWnrJ598ojoq/tRTT/mN/iqnPfPMMwefeeYZv/lKSkpaRo4cWaf3nH/vvfeW33vvvZr56L17925ZvHix34Or3W7HP/7xj0P/+Mc//LanT58+LZIk+ZUdDbR/jEIRHxFA6VGwWXAWlyQJH3/8Mfa5Q9gDeVIE6/GhLI9ZIeTh83SBWPPDaIsEm+qi/M748eMxZsyYqJftVVbT0Zpn5syZ6Nq1K/opHlSH8zB2N8ob7AhFVRGxBK1IjiJ0VW0eLnyoCXeZmZlgjKFLly6m9+lVV12FM888E9deey3Gjx+Pm266ybMMMdJDr20ie/bs8by+8sorAQgVabSErmDESL7vb7rJ/Hc5evtK6TUQ6rkqbqOVHfKzz/aW5TXLm2/6ijt6GOmIKPdRoJBsowS777koZqXAqtwPjPmLGuKIJp8/2GPOIwO0SlUaQRQflO3g6R+KSlU+6KUzBUJ5bSvF/H795Miac86R34cifAQjCkWa6mr5v57QZBS9faW89sQKTUawOtWF36MyM7Wvhc6d5XPhrrtiS+zk1/eUKcbbxfd/nz7yOX7BBda1JzlZ//NY8fhQIzk5ttpDtEm2bduWtGjRopxbb71V22QvTiHhIwKI6SuAb1nNpqYmrFu3DrNnzwYADBo0CDabDTNnzlRdVlIQSu+xY8dQqyhxpxYBcsIJJ/i8FztoEydOxA033ICHjdQVDxOxWqLWDGZTXQCvGCB+x4jgEAnMigS8Cgzn9NNP93mv3C9KM1eXy+UTFcVRCih6wofSYyNUunfvjuOPPx5dunTxeMrwY8YjqKqqqtDU1IRZs2ZBkiT07dsXJSUlqstbI4wsd1KOHI0cqd4IcRRNNDPV29Y+feTwfhVjYMMwpv0QZvWoobie//s/7bKdkiT7KRiN9urcGejYUY4o0agSZAnBnHfhesAVfKd04Z2PSZOsW7dap0J5PxT3FRcsPv8cePxxr9+IsoN299366+X3zFAjFpVREWpROUZC0Y3CmK+HibIkq90ue6lwHwRJkvfN8uXmI4b0yuQGIhzeEGp8+21o3xePn16VNOX9KzlZNpk0ExFoZapLXp5suturl3ZVkkCmnUYIZ6qLmeeW7t3l+/FFF8nv+T3LChE2UJRjLAsfBBFm7rzzzk7Dhg0bcOuttx7u27dvHKjh5iDhI8rsUri0S5Kkmz6QHEipVjBr1iz861//8puuVm40X2EgJXYex44di06dOvl1Gpt0Sgw6nU4sWbLEcMnOQBQWFvp3BAMQa1EsaiKGHloeH7EifJhNvVIKJSkpKT6eMcr9ojxPly5dqrrvlMutUKkscuDAAdTW1vrNe/bZZxtrvAmU67Db7T4+O0VFRZreNGKp3xTlA5rWvUE0J+3YUfaUAIxVpwgVrTZZLDD58c9/qk/fuVPuwH3zje/03buBJ5/07wzyY9WrlywGhYLeNitKOEcU5YN7r17GvsevbytFLCPCBwBcconcoRfvdU1NwAsvyMdQ2UnPyvIfFRbvH/w87dFDDkW/5prg2s/Ne7OzZR8TpfEiAEyfrv19o6bEIuL1rVZeFfDuV0mS03K+/lq9So4eauarV13ln4L15z/7z/fOO+bWFSyhRlF8+qn3tZZ4CnjvoSKXXy77rxjB6ogPQD6HXS5fk2uOlnfSWWd5xQMjxIrwAcj3Y/6d7t1lcVrH080U2dmAIqLUAwkfRDvm2WefPdjQ0LD2iSee8A9ZbgOQ8BFllCKHJEm6o+jZRo2hAvCVXmlAN3q+IJwnnnhC87Nly5Zh6dKl+EbZAQkSl8tlusM/btw4jBgxAg8++KAlbQgVtbQVNbQ8PgYPHgzAa8QZbZTnr57RrBZdhHx2v46+gqqqKkOi0a8qoeVHNSoPjBo1KuDyzKLcLzabzefcZYx5Kr+YQiuKQRQEExNlQ0ZA3xDSKrTuV6IZ4MSJwS//zjvlChdq8LB3Ed4pKCsD5syRo1pqa4HXX5fD4812Bs2g1U4jKCLuLEV5jNQEGrVzi9+nwlnVQM3jA5B9V4YOVe8s/eUv6j4pyjLEagaJCQmyAaHSrNIo3GcnIUHuPGldYw89pD5dEQFqCPH4aQknfB6Xy2swbCYdRKuEc48e/oKgzeYvfoSSzmMGPbHCCGJlNL3ywFr3NaOdYD3hI9iOdFWVbHCtlgKnJboef7y5VL5wCB/8mIUiBDEmC7ZW3Yvuusu3YpJyXSKxUIWMIAhLoKs5zASKdlBW5XC5XD7Ch9gpBIL3+FCi1QkUWbx4MQDZSFMPpYcJZ9myZQBMluvUwel0mi53arfbMXny5KBShMJBMKku4ndSUlKQkpJiej+EC2U7Ro4c6SMypaUFrqiVlJSEe++9F/feey+0SlmJaIlG+fn5uoJQXl4ebDYbUoV8/TvvvDPg+oJBKV5mZGT4CB82my24Y6jWOb3uOt8HtYQE7wi91ekmamg9xIth12PHBr/83Fzt0OSnn/aftnSp/H/7dm8nVYwOUXYcle1XpE2ZIpQol1NP9b6+5RavZ4MVKLdRTIfiqF1XkRA+1Dw+RMysWxlVIy7XqmuBe4Y4nbIoo9U+rd8crXLPoUZGihEfPOJt5Urj31cb6OCprWrijs0mV8GJNBrPG0GhSKXUxGSkLQD9qi6hopbSYlUUqFWGyiL8nLc6AiZcUMRHPONyuVx0wNox7uOv+VBBVV3CTKPWKAqA7du3+3WQlKkuvLPEGIMkSSELHyUlJTgglvMzgJqngsi///1vTU8SIPhKNEr2mnFUj3G0xCI1xCgR7lMRK2QovAIyMjJgs9lw9dVXo7W1Fb0MhtQbEUgA2QNnp7szm6qodnD77bcDkNO71MjKyvK077777oMkSYbXaxY14UPEiOhxjlrHl0dPde7srVxRUuL7YJaYKI/STpgAhCGaxQ++7kGDfDueNpvccXK5YuvBMVAJaLHD+sADsujCfRMAeWRcLc1Ga4Q/EPfcI69TXG9hofwXLtTuIWr3ab7N4RY+JMnbKQrFFDc/37eii1iJJVB1FKPwThwXPpTtu+EG7+tLLvFPAVErw1pWBvz73+baoby2ReHDqOGuiCjInH22nFoQSCyKpes6FAL5RqiVMw5EOFJdOGrXqlXPBeGohMf3b4ylHmtCwkc8s6msrKx/hw4dqm02W5wobYRVuFwuVlZWlg1gk9Y8JHyEGa2RfafTibfeestvujLig0eMJCUlobm52VR5VjXBQSl6KH09unTp4qkwYxV6PiDtlS0BSmBqpbqsXu1X4SmqKD1feIe+tLRU8zv3338/nnjiCUNlkW+//Xb861//wumnn46vvvoKCQkJWLRoEQDzKSpNTU0ejxylaGI1SmFDeV1rpbOJEWK6olGnTt4OnrJTmpAgP6idcoq5RoeKclTYZgttlP3444GffvKdplbSdv16YMgQ/+8NHgyoRZt99pm+ICR2IHikCWPeDnlmJnDvvcDf/+77vWCjyowajYaCkQd3td+qSEV8SJI34iCUjtcpp8jnA8fi3zIfHA71iA8xOqlvX2PLCiZNRE/4CIY33vC+7tdPPs9FLrgA+PDD4JYd62h5PXCCKXGuds2FYm6qthwRqzyVwiF8BOvxES1I+IhbHA7H9YcPH37l8OHDA0FZDe0RF4BNDofjeq0ZSPgIM1qpLlpREKtWrfJ5z4UKLnwoq7Po8ZuW87fAhRde6PO+T58+lgsfRHDwzrFZQ9RYJyUlBXfffbch8SE/Px8zZ85EXV0dvvrqK5/rRpkmFgi10rLhQilsKCO1tCI+/vrXv3pe6+6fs86SIxfUOvCRSG8R0XooDLWzfNZZ/ikZamLQRx/5Ch/cByk9XX/5CQmAWsSPkXanpwN33AE8/7z8fsKEwN+JJmrHKCNDNv/Mzpa9UiKZ6nLxxd59zz0+eIdr9GhzyxKNRJXnvoGUTsOcdhrgTv8EoB3xoWzDpZcCb7+tv2wj93Z+nDjKY8LfG/2d+OYb2ZxV7ZpSq6CmJeKcdRawcKGxdcYqgUQDNaPTQIQz4kPtvI7lVJdICKhWojxuJHzEDSNGjDgK4LyAMxLtlji5C8UvCzUeCHiZSxG9lBDuffDNN99AkiQcOXIkYNqLMs1GbYRd2QEbM2YMfve73wHQryBjtroKETzB+ILEOpmZmabKyqqJP2ZL6UYS5XXldDp92q4mfIhVXwDv9cdL9focf8ZkM0y1h91wV1NRwo9DpAzhFCWRAfg+qC5ZIv8P9ABfUOBrCssxOkqfny97cRQWGkspOuMM/2m33GJsXeHg9tuBk06S/wDZEFN5j+G/SVaP1Pbr5zUX5R4fXPgwW5JTLB0bzhFlpSDDhQ8lSuGjTx+5Gsh5Os/iRkRZMYUG8L++1CI+tAxcjx0Dli3zRk8Z+W1JSgJOPNG/Yo147oej02wFBw/6TxO9frp3V//en/4kXyfBlokN12/2F1/4T1OrLhQMJHz4E8PPGgRBmCNO70Lxg7JcLUct/UMvjUXs5D366KN48cUX8dhjj2kuHwA2bfKmOM2cOdOnbCingyI8nTGG3Nxc5Ofn+/kSiFx/vWYUkR+F4cxVbwcYrQQTbTp27Bi2ZavtAy3hpG/fvmFPZQmEUpRRVkhSE22+/PJL1WXluB38DR//SEd8cJTbFK5O6PjxgDKV6oUXvK/5vlZEz/nwyy9yp0TtgZZXTDBiaFhYKIsXRs63MWPkCjOiH1KkjpXadqakAKef7hXKnn8eeOop33m4V0Q4H/z5yDjvcKntkz//WfaL+dOf9JcVTtFPTGXini9GhA8A6N3b17NFWW57k2Y6shfl77GWF4p4n9i7V/YPeeQR+Y9/przXiJ41opCkZNIk/8/FziwXHSNFfb2x+cRKLhzxGGh5fNjtgf0/tNAzNw3H9WTVvYRSXSjVhSDaMCR8RIhTRcd+AFtVfogdDgeys7N9Ii2GDh0KQDZmVGPOnDma6+SmpHpeAVqj5jabzRNlomamaWa0vbvWaIoJuCjU1+hobAyTn5+P/gHKy6lFNlhV0cdqit1hwJ2DyYM2CI+QEDv/WkLL1q1bVU2FU1NTMXLkyPA0UIHy+vjhhx8CRquIqTgzZszwvOYC5tHdu42u3ERLLUCM+IhU9aSLLvJ9bzal4d13tYUP3oEwm3JhFHGdkfD3UK5TidhxVaZScmE9nNFmvIPIO1xq4gX3i7HbgcmTvdOVvy3Kzl+PHta2lcPvQ2pt1erclZR4X2uInADU06/UUKZyaXl8iJ44r74q/1dWM6mr877u18/Y+tUIZ6loNYym5ar9dorGt+EgnKkuPBUpHM9DFPHhT7y2myAIP+hqjhDKyAo1k0qn04mMjAyfDiQXH/TSAurEhxaBQ4cOAfCNJMnm+e8BsNlsno7Y4MGDVee57rrrNL8vjnBbkZKwfft2AOqCUbxht9sNjd7z/cb/ax3naMNFiXBWm1ETPpTGvGqsWbMG77zzDpqbm+FyuSJWEcdoqdrxQtqGmOpSJIRW8wpAjRH0KDGFeH0HOzpqFiOdw0AldLWqzSQlyRVawu3bYbdHTijSI9oP9dzjg0foBPKxGjnSKy4cf7z/skSsvt65sKI3gm3k907vd2zSJO3PuJdNaqr/urWED9EXxIhQoBfxEWsYjfAR9wHHSv8XNfSEj1Cfifjv4MSJ8n8ry7KHQ/iIt4gPJRTxQRBtBhI+IoSeXwbH6XTC4XD4iBw///wzAG/HXw01vxAR0Y+ja9euntf9dEZ2xI6bVvlbvRH+qqoqz+sVK1bots8IRoxa44WEhART1XkaGhoAAG+7DfK0on+iBRdmwikqqPmcBBLUjh07hk8++QTbtm3Dv//9b7hcLsOCRKiotU2t7eI+E42QxfsFvx84A50zN94IKMyKI4IY8RFJf5GHH/afJgqKkqSfrlJeDmzerP5ZUlJ4H3YffBC4//7wLV+J0YgPLcKZksM9Pvg93si98aGHZNPQPn3059P53QyKvn1lLwVlR+7MM+X/Vtxf9Kpd8SgPtfUEW9VFLGMrLiceMHpeiqlE3Pdk717r2yOiV9UlWE47Tf7PRf/cXDmFKTc3tOWKUMQHQRBtGLoLRQhRcNBCTfgwQqDoATHV5RShxGUfnYdGseNmppIMx+qyq4HKv8YTRoQPsZPMjy/3hclUc9yPIpGM+FBLYdGiXsj/rq2thdPpjJjwoVxP165dVYUPI9FQLS0tGPftt+gwf77+jJ06AQMHmm6rZTAWuCyklah1ekSDWKczdh+0k5MjG+0RrPDBo/3CKbbytvFzx8g5bLfLoofRTvpxxwXXNiXKtBx+z+O/2WZEA6WfCgA88IB++pOWkTDgW9VF79wSBiUA+F4zwWKgNHlYCOb6DrfgwQlHqguPqOMioZX3N35OhcPjg0fXxYuoRlVdCKLNEqNPhW2DnTt3el4bETOcTmdQo9JHNUI2uY9Ely5dPNPEzqleR1VsQ4mYm2yQlStXmv6OHsqKF/GM0YgPZTlbjlYETrTg7QunqBBMZZtffvnF5300Iz5CiVg6zt1pS4qFtAg1xG0V7jVRQRy9djoBpVCmLI/bXghW+EhNlU1Qw4nSlDMcUUNWpW/wzqwy4oP/N+PDpPabFmhf6wkfYsSH3n3ymWe0P5s6VX/9Vn8vVPbvDzxPNKuhWW1uqnzWs7JDzoXkcER8aPkpxQvx3HaCIHwg4SOMfP7556bm379/P5qbm02XLV28eLHq9Ly8PNhsNh/RRez4GRU+Bhh4aNRrs9kIFjXCaZwZacymukSqsx4sWgKN1etgjJmqbKNW8SiaHh/bBENBvs/U0s0uv/xyn/dnuMugJkarWotRojGid/fdvu/FtD+xXCVHGRIeipFjW0E8ZsOG+X7mcoU/aoYvXxlFESxqv1dWiSk8LUdL+DCL0aok4vrF/2qfuVzmOvvuVEoAwQuXotFqJKuPqZV1VSKKUV27+kcvhUsMDUfEh1YlHyuXHYaIj73N0a2yFjIkfBBEmyEqPSrG2F2Msc2MsU2MsbcZYymMsTzG2FeMse3u/xYmLUYHsx3B1NRUNDQ0+JSh1aLAgIngd99959dRFNukV37UaMTHaHflA70Oqd73y8vLPR4WeqS5zQxF08d4xai5qTi/yAknnGB1kywh3AKNzWYzFEnEKygZ8dUJF2opLF999ZXf53l5ebj00kt95uupCBtPcY8Ce9J8Yi3yQ+yMRfoBMSsLKCryej2IHQ23KawPYoWr5GTAoNlz3GM04uPYMd+O688/+3aMwwFv2/Ll8v9QRYrp02XfAxGrBE/emeUiJjecDrbNK1aY6xxbEfGh5P33va8NGEbrtgsA3NXkYoYffvC+zs/3F2bCZeYaDuEjnL+xYYz4qHElkHhAEERMEHHhgzFWAuB3AEZKkjQQgB3AJQAeAPC1JEm9AXztfh/XlKs9eOtgJjLiggsuwLnnnmu2ST7+EHpVMcTSqcqKNCLcaFNZapVPT0tL0+3k//vf/8Z///tf/UbDK54M4a72cYyRyAW9CJrS0lKLW2QNVlTv0cOosMLP6yplLjuA5bxzFWbM7Avldim/yyM9POdErAlfYnuj8XBrs3k7M6If0cGD6vNyIhHNECsYFT5++w3QiCAMG7xtXNALxzGxapm8M8tLofK0w2CFle++MzfCHg7hwwrE9ixbFvxyGhut73h/8433dUKCfN2Lv7/hKimtJnyEmuqSlxdam/QIY8RHVNON2jM33wzccku0W0EQMUW0nvoSAKQyxhIApAE4CGAKgNfdn78OYGp0mhY+uOeGFkrxAAB+//vfAwDuVJQrs9vt6BtkDXcjnbJ97rJ3gSIs1MqMAsDQoUMByBVJ9mmU0OPfMeLfkZOTAwDo3bt3wHljHZvNZqqcrZLU1NgMG41ExIeZ+dRMedWusXAQqK3isQ10PXLhw5PqEmPmtlGN+ODr5Q/WRo4vvw+3ttIoJOAvChgpeWol/Bj06iV7ilh1TIRS0ZYJH/v3y+Iaj76ywpdEKP0eEL4dgYQPtd+XQAakVkXImU3fEXniCeA//5FfSxKwfr3vtoRa0p3fKx57LLTlmFmXlXTvbu3yRMLp8QGApI8oUFQEFBZGuxUEEVNEsPagjCRJBxhj/wDwG4BGAF9KkvQlY6xQkqRD7nkOMcZU8zAYYzcCuBGQfR/MRlUEolqt3nuInHfeeSgvL8e4ceMwePBgvPPOO57PGGOekVxxhFrcrttuu83PE6K6utqnc6W3H5Sf3XrrrQG/w3E6nbrz8fD7srIynw65Mn1FbRmiB0OgtlS6w2erqqpMe6BEmkDnUGtrK1pbWwPuV0mSUF5e7lOdBJCFD6vP+1BocT+4NzY2RrRdWutS7i+j37OSQOdAXV2dpx1KgUatfYWFhUhOTkZDQwOaUlPhCtM2BHP/S6mvh62hAS3V1XBWVCDVfe03ROhcSK6vB1pa0FxejoTkZCRppGa4CgvRVF6OtFWrPNNaa2vRGkPXktWkGTgWtqoqpAj7zFlbi2b3/EklJbBVVqLJ4D4K5vxJqKlBUkMDHFVVsNfXo9Gi45FQW+s5F5qqqiy5ZtL27AEAOPPzYS8rQ1OfPnCVl8NWXe3Zh3r7Ok3l3Gw8eBCpDQ1oPvNMOAO0MaG6GkkNDXAlJvodE1Zbi9SGBrRUVKheA42jRiF140bNZTdPmhRw/XrwbXPW1HjOn6CW0dCA5qVLYd++HQm//oqWU0+FY9QoAEDqs8+CKbx7At1nxH3eWluLhNpaMEFs4t+3+tkvua4OqK/32Rf2ykokNzSgsaICUjACg8vl2R5XQYHh69IIyY2NsDc0wFlZGfTx06K+rhZ1DY2WLzdcJFRV+VxDRn/LwtF/IAjCWiIufLi9O6YA6A6gCsD7jLErjH5fkqSXALwEACNHjpSMeF2YxepldurUybNMHrnAEYWP9PR0MMYwduzYgG0oKChArmDUpzd/KNuTmJio+/2KigoAgMPh8JkvNTXVZ9vUllFWVuZ5nZ+frzvyvWTJEgBAdna25ccnHOi1ke8bvXlSUlI882QoQnH1Uo+iAY9EyMzMDOuxEb1OcnJyNNcV6OEjEudPoLS1rKwsTzuUQo1a+/bffz8S1q1D2tSpSAtnuLPG+nXJyAAcDqTl5gIdOgBuP560SF2n2dkAY8gsKJDLPaalyZEDfFQ+NRW49165qTabp30AgJwcb4nItoiRY9HY6LtPMjLkfQnI0UUOBzJM7CPT509urrz+jAwgIwPpVh2P/Hzf7bdiuXw/degAHDuGtG7d5PbzfZidrb+vxf3MJ23cCKSlIa2qKnAbhX3ld0xSUuTlZGX5r2fMGLmtKuv3tGPoUPlaCZarr5b9QkaM8J4/ZuHH69tvPe/TEhK8+8Vu99sGz/6urJSvZ+VzhPJ6T0vzidARj5elvw3udF+ffeFef1p+fnAloiXJuz1ZWaauy4Dk5QHV1UBKSvDHT4OjGelISc+wfLlhg58nbsz8lsXD8ylBtGeikepyGoDdkiSVSZLUCuBDAGMAHGGMFQOA+796jdY4gnfkRbFDaVQpdvadTickSdIMk+8uhDlGqjpFoDD8rVu3AvAt3QsYKzsqLtuo2WesR3sYIdRUl1glkh4femleemkmsXLdaM2bpyVq2GyoHz48vDnewRJtj4+9ewH3SLwnTFs0gD33XDlFgJ8XokdKe/H40EPvmEWiDKVYjcTKdYnXutXHWenVwDvSwaS88BKlalWIlOjtH2VZ4E6dvJ8NGRJY1Ag1hZJXhLH6N9rIs0F5OfDss7JnihbXXhue9BM9rF6XePyt9uLgqS5h8PhgiLNUlzbwnEkQhDrReOr7DcAJjLE0Jj/xTwTwC4AFAK52z3M1gI+j0DZLGTx4MLKzs32qS+h1ylrdD+1a84jVH/g8fXg1A4s55ZRTDM3HR/tbVPKUzXT+AgkBSe6OTFYwoyQxhlHhgyPux1geTYikx8fJJ5+sOZ+euHHeeedZ2iYtAp374ufidp1zzjlha1PYiZbHh8hnn8n/xQ6oskMnRlBFu72xgN51G2nhw0rEcyDcwkcogipflrt6ky5mhI+uXb2fqQkyU6YYa59R+HoXLQru+1qpH0Y6oTwVQTEA40PXrpEVPsJhbgp4SwdbnTbCz5EweHzQXZYgiFgh4sKHJEk/AZgHYA2Aje42vATgcQCnM8a2Azjd/T6ucblcuhEeyvdc+FAzZQR8O3T8NS9Ja3UkBBcaAplB8gojSkFCkiSfbVu/fj228RKAbsR0gEBCwKhRo2C326NaotQqduzYgYaGhqCMNgtj2KgqksKH3nngKfuqQoqRzoUFmDE3Fec9duxY2NoUNqJtbqqG2NFTHnPx2FDEh/8+EH9LIiF88PXHU8QH73TqVVkxyrBh8n8j1ZqMCB/8d0UU+HjU6Q03eKd17my4iYYIdR+HInwtXSr/55FfWthsvud3OIXmcIksfD/rpC0FRQjmpoY2M1Z+GwiCaNdE5alPkqSZkiT1lSRpoCRJV0qS1CxJ0jFJkiZKktTb/b8iGm2zEpfLZaoDxKufrBLM97Tm5cvlAohS+AhVCOGiRKDO+UknnQTAv0POhY9x48YBAObPn+9j6qpsYyDhw8i+jBe4iW2djkO91vGLxfQXbrzb1NQU1vWIx18vqkNpBCwSqf1npmpL3J/X0U51KS31H9EWxY7iYt/PxP0dg9dTxAkU8RHu81OsRmIl4RQ+3N5WfudPMOcT9/gR07O0MFKWmEdfutNQAXj3RUmJ7Hdz3XWhVaJRg1ebOv744L5v5viPGeP73mhnnTFfgSVMEbOedYVD+BCPpZUEKXxIErApUGJ6vKWOxFt7CYIwTJw/ccc2ZoWPQCKDOC/v+HGBwuFw4IcffsBGt2s77zj369fPfMPhTWHR60QC2uVs+fr1RubF7Q0kfDidzoj5M8QK/Hib6URHg/379wMAfvzxx7Cux6hAoLePIrX/xLaqtVvrmHbr1i28DQsH0Y74yMrydrp4h4iPhqqNiq5c6X0d76JTIG6/HTj/fP15oh3xEY8eHxylx4fRUtNiqh5fhpE26s3Dl8M7rvn56vOlp3v9OKyEX//BRmWaifjIyQFGjfKmfRhFKUaEM3VW7Vy2ItUlXOcyP4dNenzsqARWHVL/zOkCmqy3DCEIggiaNv7UF10CCR9du3b1qdDB00WM+GsoIz6cTie++uorfPjhh551A0CxcrTTIEYjPnjZ2uXLl/t9xhjTFSvEZQdaT1uK+Ghr8HP49NNPD+t6+PEPJA706tVL87NYifjQSnWJZQ+XgERL+BA7M4zJD/D8vqNW2raiwve7bZmCAtnYUo9Y8viIV+EjNxeYOhW48EJj35s40SvSmekMG0l14QRKZVFUmLOEUKIczHyPmxVzscRoCiPfR8cd5x8JZjXhTnWx+roMMuJjfw3QrCFutLqABvfiJEbPbwRBRJ+Il7NtTwTqrJ966qno2LEj9u/fj7ffftvT+RfL1GrBO06i8KFcNxB8GD0vNVtTU6M7H0/XOHDggM90nupiVPhoTxEf3bt3x+7du3W3OZ5SXfg5lmZ1zrHGegKd00lJSUhKSgrZcDcUtISNQJ/HpbgX7YgPsQPkcsnvjbYjHve31UQ74oOvPwjPI13E3wveqbMacd8MHWruu7zjzbfbyLloxtyUMVkQ0BKCw3FcxWvRLAHKkPvAmO+6hg0D3JGHfhQXeyM7wiWyaRFPwgeP+DB5/NYdBk4tBWqbgSQ7kCz0KlwSUNUESh0hCCJmoKe+MBJI+LDb7UhLS8Nxxx0HAFi3bp3hZQcSPnjHOdiO1MGDB021Q4kR4UP0uAgkfOiV+Y03hrnN7MykNulNixXC3bbDhw8DMFaSVmueWEl1EYnlY2qIaHt8iCOrvKOut8+FKDt6IIf+MYvniA/RwyJc2xDKcvk9io+wG1mWXhoCP+d5WVzGgAceMB6FYgWhRDloeJtpIgofXNhSE3nE8ypcRrpqhKuqS7ieg0IQB/PTgGONvmkt28rlVJdNR4FKZ5iEx3BBvwsE0WZpGz3JGMXpdOp2enhUhRIzHSGtlBQuJATbqeps0PFdjE7Zt28fvvrqKwBe4UWvk8rnBYwJH3HfQXSjJVZpMXjw4HA2xzKiISpoEW3hI9hUl7gkFiM+9DjtNO9rq0uoxiPRjvjg16rVxyKSqS5GmTHDW0aWVyD5+Wf5v5E2fvGF/L+yUrst69eba5OV8GtRkoCFC4GjgVwvBdzCtul1ffwx4E7xVd2H4j1BrHwTDeHDCsId8WGCne7T0M7k6I4tQoXdFieweDewqxLY2WLSiyXakPBBEG2WOH/ijm0CRXwoq2BwsUGvczZlyhRMmDDB85538JQmpKGmuhg1Re3UqZPn9ezZs/HDDz943geK+BBpj8JHoG3mJCQkeLxaYnEfVLtDlIMpzxsMRs7pBI2HuFiJ+GhTwgcnFjw+lB31UaP85xfvSXx0vD0T7YgPMdUlXB4fsRLxUVrqLV+rrOpl5D4waJD8X+13NZQKM6LZaijY7fJxrKkBfvoJmDvX+HfN/n7YbPJ31q7Vn09N+IiE4KknfFgR8REujw8T1LuzST3ChzCW1+QA9lTJqS/HHEnx5acUjybjBEEYoo08cccmRlJdOFlZWYY6ZUOHDsXJwkNKuDw+jH5PbT5JkgyluogE6jS3JeGD7zO9bVZ6fPBtj8VOMhfwdu/eHZH1xVvEB6W6hBkx4qO+XjY05dePmoHjIaEEAY3saYfj89eRivgIp8dHrER8iCjvUUaWxQckjJh5mmmbu+x8yHDhIxjMipBqx1Rtm9WEj0imcIlYcb8Jl9dZEMLH18JP/jd7gJpmoNV9+Lcdk/83OwAX4uw3rrgYeOSRaLeCIIgwEHu9qDZEIOGjj1BD3mazeTrCZjpCgTw+gu1UGRUsQhE+CgsLPa/bY8SHGY8Pvp9jeR90CUeJRBXiwePDTKpLLB9TU0RrO8SR1S1b5P/19drzi2IIpbqod6I3b5b9JNpKxIdVwseJJ/q+D6W9yqg0M23UO7+DqVYSRJqDKqGYm1ZVGZ9XkozvrxhLdQlZ+oiRVJdat06V7L7MePSHw334y9wFtUhaJggiliDhI4wEEj5SU1M9r+12u+HUBxEtj49QhQ+zaQvierjwAXjL3aohCj+Btt3lcrWZDqJZjw8gtiM+OIHKzFpFPER8iARKdYl7RI+PaK1f2cHYuVP+r2YY3bGj9zVFfMgjveLo5pEjwPvvA1991XaED6uWy1NNrFgurzTCMXJvD1BlDRkZAK+uFY3rMZSIDzPs2wccO+Y/XW2bq6tjxty0xQlst3VoE6kutW6ho08+sHSvd3p9q6/JqYe29JtHEETcEru9qDaAy+XSHZ1W+gCEEvGh9PgItaqLmTYUFBSgf//+futmjPmlbIjlccXP2lNVFyPCh3K/GS3lGg349oS73PBJJ50EwNg+aGxsVJ0eDcEh3qrzmEYpfFx6KXD77ZFbvzjK3LcvIESSQc1AOtqpOfFCdXVkOojhSnURR7Ctum8qlxPKvhkxwrplicsQy9lGmlAiPtT8eABArcJccrK6iatSyNy+Xf7PfUDCVUFIC0V7JFiQ9hGuZwCTER+bBN/aA7Xe18cagG92A0UZwswkMBMEESNYFN9IqBEo4kMpfLS6y9pZkeoSalWXpKQkU20Q19/U1KSZ6tLQ0IAs5UgX2meqS6BtjrdUl3CLMny/GVlPlUbYdKwIHyLJyckAgLFjx0aiOdaj3D4hkiti6xcfrMX29OwZ2ba0JbZuBYqKKOJDJBQDUSVKodjI/TNQB1IUPoxgtY8Bj/jgbTDzm1BQoD79t9/8p23dCkyeDHzyie90ZdSC23jbQySFj3ir6mIi4qPJAbS6D3FWsu9nOyqBX1WCcQiCIGKB2Bs+bkP069cP3bt31/xc7MApxQOjhMvjIzs72/C8R44cwdatWz3vFy5c6BEqMjMzfeYV22k24iOWO/1mMGJuqiSWU11GjhwJIHLCRyjEyjkktiMxMREPP/ywT7WmuCLaqS5lZd7OljI1Q0xr4VDEhzFKSyOf6mIl4fD4sDLiw8plicuMZsSHXqrLpk3qZXg5Wt8TI7g4Lpe6N43yOUJLqIqS8CFJkhzvEeepLp/8ChxxFyUaVeL7mZboEZcxH927GzMSJggibqCIjzASqCOjHNEPJdWFR4twQhU+QqGlpcUTMdK1a1efz7TKjLanqi7BeHzEcqrLpEmTcPrpp0dM+FCmAZkhkudQdnY2qqurDaW6aF0XcUW0rs9t2+T/tbXejnpeHlBRETjiQ61TRchkZckVciKZ6hLrER9KrBQ+jGAm4iOawodYXpozb56covLgg+rf1fo9VKvMdMopwVVNiQFz05CJgVQXpwuoNlGEZ2hKVXyKzFdfHe0WEARhMW3gabttsH//fs9rM50z3mFqaWnxmR5N4YPDGPNb/+bNmz3VXNprxEcwHh9822NxH5gpWxwK/Fw3YwKcn5+PY4IJXiT3XywfM0uJ9vadcgqwbJncIeDXzR13yJ329HT97w4cGP72xSuSFNmID6tH4SNwTwqJYDqwgSIxzaa6WE1iItDa6r0OlW3RK1mr1W418SAtTX3/Bdr2SJub+k8EQ4jXVIxUdQGAHAPBEIk2oIPdZKligiCIMBF7w8ftFJ7nb5ZAER/RiBCoq6vTFCq28dFZBVTVxR+17f1NLd+5ncCFDzMRH4MGDcLNN9/seR8rwkdbOZd9iNY28RFhMdWFMW3Rg4d09+4dkebFLZEWPqwmHO22cgQ/mPb17y+PQs+cqf55tFNduPioFvERCK3fQ7VlpaaqnzfK9Snfi6ku4X42Uov4sOL84YJeDAgfJ7kr2Ce4d+WADv7zTOgOpNkiUOmHIAjCABTxEQX+/Oc/+00rLS31iAJmOkVc2FixYoXP9H379plelpKsrCyccMIJpr93+PBhFBYWqq5b9A5p71VdzEQu1NbKtul79+4NMGfbJdHdYTVb9jlFyNGNZEqJniFtmxI+ou3xwTsCRjt8OTnARRcBPXqEtVlxz+7dcn57pFJdgOhHDwXCSuEjmN8zxmTfAb3Po1lBQ1nVhb820qZAER/888JC+drdscP4MjiRTHUBVDw+LFhmuJ6DTERIudzbkeL+OU1LBGqagQ7pAIRCWn0LgP4dgCp7S+xf2wRBtAvaRk8yzlBLARE79WY6RXxe3jHmfPrpp6aXpeSuu+7CiSeeGNR3tUble/To4ZeWA1CqSyBC8bVoK5gxhR03bhwA/5LSkRQ+9M7XtnIuA4j+A60Yvm40QqF/fzKtC0R9fXxHfMQ64div0fb44MKHMkrDyO+X1n29rs53GQMHyttmJNVFy+w0WlVd+PtYTHUxIXx0z5X/F7qD6rq6x7NyhVtqVjJQkOp+Q88vBEHECO30iSP2CNYjIS0tTffzSHSwlAamgLZQ8cUXX+Bvf/ubZx5OezI3NdKBVwodJHyYi5Th+1hZUjrRhHN9qLQ7j49obadYFSQSHfX2QmpqZPZnOCM+Tj4ZuOwy65aXl2fdskj48EXr9/DwYfm/skSukVQXLeEjSuamEiB7fIRClFPDGlqBVvehSnJfuoM7AoMLgdIc73wpCUAyxZQTBBFjkPARI4Qa8RHs51ZwzTXX+Lzv2rVrQKHC4XD4vKeID3/E7Y2EeWisYybig+87SZKiFvHRblJdONEWPsQOFxE6jY2Rj/iwel0TJwLHHWfd8hITgTvvtG55VqNMNYk0XHhRpqeEkurC4cvQi3gIZKYq3iui4fFhBeGK+DC4vCYH0KrQ1gozgI7u6A9ueCpJgK0N/swRBBHfkPARIwQrfKghRgdEqoMltr9Tp04+6x48eLDf/FVVVaY8PtqSuamZDjyHd9gnT54cljbFA8FGfIjCR6xEfLSVcxlA7ER8RHOku61CqS7+xLIIHSsRHxyjER/V1YBiMMQPI9tVWen7/ttv/dvHiUTEh+K3SpIksFDXHWXhQ5KAvVX+0wcqjE1LcwSzU4rEIwgiRoizJ462i5UdITOCglVccsklPusU2zBlyhSPGKJFe4r4YIzBZrOZSnWprq4GAJSXl4e1bbGMKGaYmTda0TJ6ER9timhvXzAeH4Q/WqkD8ZzqEg5I+NBGmepiJOKjuhp4+mlg3Tr9ZStTXbj3h0hNjfp3TzlF/h/Jc81uD0/0TZSj2iQAda3AsCLf6Xx3ds4CumQBJ3aOj8uZIIj2BQkfMYLYObMy4iNS3hBKsUUUKmw2Gy6//PKA3wm0/LbUgbTb7QG3WdzeGvcD3bpAD4dtGDOmsGKqS7TOm3YT8RFtSPiwhnvvBcSy6jk5FPGhRrwIH9EgGI8PNQFDDWWqi5nzpn9//+9EQviQJJ/jIYHJHh+hrJtX8AsUIRMmXBLgdAFDi9Q/75QpV3exx9llTRBE+4BuTTGClakuYoc6UsJHpRBiumrVKr8Op7IU7cGDB32mGzE3bSvlbAG5E28m1YXTlvaBWTp16oRu3brhzDPPDDhvY2MjAK9gFA3ajfDBtyVaI5FiOVvy+Aie1FRvBxGInFmsuPx4uC5i+R5ss3lNQuMl4sNoO5WRLGrfy8rSX4d47MJ9HHl71VJ/QqFLF//lRghJAja7y9VqHbU++b7VXTywGL5uCIJoN9CdKEYIpUM7bNgwn/fRSHXJz8/3myZ27lIUpSN3797tYzzZ3iI+mpqasGfPHtPfa0v7wCyJiYmYMWMGiouLA87LI2O2bt0a5lZp027K2fJ7V7REB/L4sA5x3zkcbofCCJhAcvEqHo5dLEd82O2xIXwo0bs3GD2/lKkuamitp7XV/7vh3j9Ll8r/f/vNM0mu6hIiAwe6Fxad+63n8GpsSIJN8Pbgs5IgTRBEjEDCR4xw4MABz2uznaKRI0f6vBcjCSIV8aH08HC5XLpiBu+YGvVtaGvCBwAcPXpU8zPlcUtKSgIAjB49OqxtaitwQ93CwsKotaHdlLPlRFv4oHK2oSN2DCO5P2M5ikJJLLdVFD6igTLig2NFxEcwVV2U342Gn0xDg087Qk512bxZ/s/L/EYYfiTTdAqkUZoLQRCxClXZjhH27dvneW22o9Shg6+8fujQIc/rSEV8pKWl+bzftm1bwO9wMYMx1q6quhhF3N6MjAxUVFSgV69eUWxR/FBUJCcgq0UiRQpKdYkQ5PFhHUrhw26PzP7kfgXxcOxiuY3cVwKITjs3bwaamvzFl1CFj8ZG/4iunBz/+QI970Qy4oPj4/FhAfxaEQWVCCHBuw2JMRz4RBAEoQXpsm0AXuqUkyXkuUZK+ACAmTNnGp63c+fOALwVTtpjxIcZeARIe/b4MEOvXr3QtWtXnHrqqVFrAwkfEUIpfBDBoxQ+XK7IdBDb63Hr2NHa5fGUDiA6wgc3KnV7LHkIVfhwOPxTXYpU3DXNCB/h/i0dMUL+X1Bg7XInTJD/p6gZaYSfY8HoLSRIEwQRI1AvKkY47bTTPK/NdoqU84siQrRKeQZi//79Pp15I+ambaqzGABlqgt/H6vHM9ZITk7GNddcgwL3Q+dtt92G22+/PaJt4OdroFLOcU+sCR/t6D5hOcrOoMMR2f3Z3o7d9OnWLm/XLmuXZxYe+ak8jqEKH5Lkn+qiNZ8ekYz46NbNb52SS5KtMUJZNzfsFgzlI0V9C3DIYBEegiCIWISEjxiBezgA1lR14d4GAwYMCGlZZhG3wwiMMUOlXdtaVRcjiOcBRXyERkFBQcTTXvjxS0lJQd++fVU/axNEe7RerOoCtL/Os5Uo7y9OJ+3PcBJOITsax238ePm/mVQXEV6xRInLZez61nqO4Ps5kh4fq1fL/1et8pnMQk14ieL9lkQPgiDiHepFxQhWdmglSUJSUhK6d+8e8Y6ysoOnh9iZb6+pLmbNZyniI374TXTzj7Y4EE4yM+X/2dnRWT9FfFiH2r6LxP7kJsTt7dhZ/ft83HHe19HYl3yd4u95c7N+Z138LD3d+/quuwBu3C6msYn7TCxf26OHv/DBxW5eCSySER8nneRtl3eloa87IXrWfLyU7TCVLKOAtLdrmyCImISEjxhB7NBaEfERLaFgw4YNhuflbWzPwkd1dbWh+XjHuS3ug7aKw21Cp3bM2pQQcsYZwCWXANFK6VFWdSGCR60jHsmqLu3t/ma1kK1m+BlJlKWlAaCqyrjwIe6P7GxvuogY8SGeo7y0KyBHiyifI1JTgZ49vedVJD0+uCAsrseK+5MoDkWYSrd1S36qyS/SfZkgiBiBhI8YQYzMsEL4aGxsjHrnasiQIbqfl5eXGxY+2mpVl+3btxuaj++ftrgP2gODBg3yeR/ta9NSkpIAE5FelkMRH9YRbeGjvWH1dovHKhrXgShCcg4e9O346t37bDage3f/6aK5qbhd48bJ/wsL1b2GlPeDSEZ8cBFH2BcSPDEfwdO1a6hLCIpWJ1DdHJVVEwRBWEY7fdqIPWpray1bliRJOHbsGHbv3m3ZMoNh0qRJPu+VJo8N7nJs7TniY8eOHarTldtLER/xR0lJCQD5mCm9dtqU8BFtlKPMdI0ET7SED95JbG/HzuqIj2jvP7VUl48/Bt580/tez//Dbgcuvxy4/375/dq18v/vv1dPdUlOBs47T44427hRnlanY0QRSY8Pvi7RhFRyF4S1Yt0RPtZby0P7Pv3iEQQRC5DwESOsX7/e89qKiI9YQGl0eu211+Lhhx/2vDcT8dFWhY9ff/3V0HzDhw8HIBtlEvFBhw4dND8j4cNCKOLDOprdQ7qRjhxor95F/Ny1avujXc5WLeIDAMqFXvPBg8AjjwB798rvxXuhzSZ7WKS6cym4R0d6urawOXw4kJsLHDsmvxcr20Qz4oMfi+XLvc2xYrmJicDgwcDVV1uxtKDoa7pCL92XCYKIDUj4iBFCNa1M42XkEF3hQxQnlMaqdrsdCSrGXEarurRF4cOo+eypp56Khx9+GImJiWFuEWEVPIqrzXt8RBuxqgvt19D47jv5v7gfyeMjfPBzV6uaiVkaG72voyl86P2e8/TOzZvl/1oeH4DX3LRrV3WPDzX27AncPiPLCZXcXPn/ySd7Jklgoae6MAZccAFQWhrqkrQRzyM3h+u9rxPbqU5JEET8Q8JHjCCmgQTTwVdLi4gGPDIBMLYdPOLDqRwhUtDWhI8+ffoA8E//ERG3lzGmKhoRscvOnTsBABUVFX6fZYnVCIjQoIgP60hVcS2kVJfwYbcDN9wAXHqpNcvr2dOa5QQLP35c1FCDf7Zypf9nSuFDeW2L05RwM1GRaEZ8qIl5kmSByUcEqKnxm7T9WBTaQRAEYTEkfMQIo0aN8rwOpoMvRg5EM+IjmM55e0x1OfPMMwEA/fr1U/2cIgLaLjfffDMyMjKi3Yy2gzK8vg3dJyKOmgBN5qb+dO4MWHUNl5TIXhVWEEkPCzX4cRRKefuhFILF3zq375ff8iQpsIePGPmlRST3j4rfiQSAxcNPu/I4AGh23xpyzVZ04dB9mSCIGCDOnjbaLqGmuogdqUDRE+HESCrG1KlTPa/NVHUxmhYSD/DUJBI42j5KwS7Ua51QIHYwKOIjNFpa/KdFYn/GmzHt9dcD99wT7Vb4E+3fyLo6bC0H/rcWeOZH+b+fKSavcMVTNcTfwA0bfOcVIz4CpboIAujeKqDJAf/7QSS9a1TSfqQIrNYSXn9d86Ogmk/POQRBxAhtpyfZhggmsuHSSy/1pJlw4WPs2LGWtssIqWqh0gqUo93tMeKDR8Y4HI4ot4QIFzyah1KUwgxjcieDPD5C58QT/adF4r5r0OSZCECUhY/1C1biy51ATbPcya9pBr7cqRA/+LHmUSF616xS1AS0t3HoUPl/cTGanRqL1Up7CQdqER9xkumiR2fK0iQIIo4h4SNGUPo5mCUzMxOjR48G4BU+lFVVIkFXd435LgbN2hhjaGlpCVh6t60JHzx65ccff9Scpy1tb3uEXwvKqB46rmFAFD5o/wbPSSf5T4vk/qRjFxqR9LBQYcGuBDgVgoNTAr7dI0zglYOMpOSqRXxobVffvvL/lBQw5q6gonc/iESqC78vuZGFjxgWZ887L+AsOVRYjiCIOIaEjzYE70xz4SMaqSGdO3fGNddcgxkzZujOI3LkyBEA+t4kbU344DQ1NalOpxSY+Idff3QsIwAJH9aQkQHcfTfwpz95p5HwET9EOeJjadeTVac36QU26t0fzaS68PKxn3wCFmCxACLnXSM0pMKZCFssn+IDBgScJTsYOxq6LxMEESOQ8BEj5OTkeF4H28FXCh/REgq6du2qK7okC0ZuYhsbVAy1OG1V+CDaLvx8VQofJISEAbs9/nwiYpWsLF8TyMOHo9cWwhzu391ylh6V66DVbrLcemOjceEjUKqLMHDiifhQg5eZjcT+cTi8JaIB7GuO8XAJA2mZXbMj0A6CIIgwQcJHjCD6AMS78BEshw4d0vysPQof7W172xr8+CkjmUj4CAM2m1yRhPat9SgNJy1m/lbg2a3ZeOZH4JGl8nsiSNzPAIds0TFiSE/1Pse8fvLtntepWv3pACmuPhWbAgmbYrow3LcCtUgDPvASheiYmP9JF/dJXZ3qLAlB7ja6MxMEEQuQ8BGDBNvh5d+LJ+FDbGONSu14Tlur6iKi1hGmznH8oxXxQYQBSnWJS+ZvBR74Gviu4whIAKoaJfz+C+CP30S7ZXFKlH8jb52UD7v78qvMKEBzYgrsDBhfqvGF7Gx9sZL7lLW2Bo74yHaHIiQkyMKH1jLdqbXYt097veEi1u9PYttWrVKdJZhUHdnXJIa3myCIdkPb7EnGOaFGfOxz/6DHg1BQUVHhef3pp59qzteWIz4CVbQh4hMewbR27doot6RtM38r8OzPNtz4sQvP/yThxwNt8z7RFnnyB6DRAbiY/FvFXC5IAOZupMiPoIiyuenkkVno/pd78f70mWAAOtiacEZPoG+Bdx6x3O3k9+z4coeBVJfW1sAeH5mZ8v9RowDomJtyAWXHDjObZgkDU2ojvs6gWbLE83JLWYjLIvGfIIgYIfZ7xu2QYDv4zW639K1bt4a0nFiDj5i3le1RQiVt2ybHjh3zeZ/rzi2nCBDr4BEDlS02MMmF6mbg9Q2MOs1WEsb77kF3P1Diwod7nF6CLIoQJomBwY5zh6fjh+sY9twJXDe1m5/osWiHt9zt0Ron/m+p5FvuVo3vvjPm4ZOWBjgcxk5Z0ccmQuxoTo/4Oq3AQWMzBEG0EaLyK8kYy2GMzWOMbWWM/cIYO5ExlscY+4oxtt39PzcabYsFgo3UsCt+yNuKUNBWhY8zzzwTgDc1SUlb2972hlLgUF6fROjwiAGJ2WCTXGi1JaLJxajTbCWpqWFbdCf3IL0n4kO4Zg7G0eB4zBADwocPffr4vF28yzcFxSa5IEkSFu9yT9C7RwZKdQGAlhagqQkMwGe/Av/8QcKtCxnGzFaJICosNLgR1jB/K/D8Khv+s1qjPTEM3/XpSdFtB0EQRKhE61fyWQCLJEnqC2AIgF8APADga0mSegP42v2+XRJsh5ePKIe6nEgzZMgQ3c/bqvDBDW1beRk+AYoKiH969+4NAEhKoqfFcHHA3Tl22uxgkgsOd1WJA9Rptg63QBsOSt22DFz4EOGiCGGCKKe6+DFmDHDGGZ63rYrIAZvkkgVLPl1P+PjyS/eXdB5bHQ5g40Z8tQuYtRSodkeWHKiVI8Pmb4U3JWbCBLNbEzQ8Mq2mRX7v055Yo0MHv0khP43EurcJQRDthogLH4yxLACnAPgfAEiS1CJJUhWAKQBed8/2OoCpkW5brBCquSknHjw+AGDcuHG6n7dV4WPLli0AgBUrVkS5JUQ4KC4uBiCXdxZpa+dxLOByR3yATPSsJ9FkiVIT/HhA/t/voFw5prh6v+ezCaVhW23bJS0t2i3w58QTNT+yuZye9Kb/rQWeWdbiHw3RpQvQvbvs8wEY6kD/ZxXQ5OQRRPL8jQ53+hR/LjJQutUqeGQaAE/7Pe2JNUioJwiiDRO5O7+XHgDKALzKGBsCYDWAOwEUSpJ0CAAkSTrEGOuo9mXG2I0AbgSAzp07o7w8UHKoOaqrqy1dXjBUVlaqRgGYpa6uzvL9YxWnnHIKli1bBgA+bVRrL98XjY2NMbs9IkbPIT7f7t27/barubkZLpcrLraXUCcjIwNDhgzBsGHDUF5e7klpqqioMC1+1NbKIQzhPh9i4f5nhp7uypR9GnYBDcDRjEK4kmrQM7kcdOmERnLHjrDv2YPmY8fgNLgzzZ4/pe4+Vr+67WAJcge4Z7K8rg17QMfQJKyuDqkNDWhKbEJFRQVcMdKJTWtoAADkKZ44SxPLYZMk5CU0AE4gNwFIaSnHf5cDkL+CAz82oL5ZQvfaBgzqCBRUVmqKcXw9Ca01KE1qRUFCHVhijeecYi1AXUMDbA0NaKyshCSIH+G49/H2ZDQeQufEFOQmNCDH1uDTnlg7x21DhyJl+3YAQIO7cfvKEtHQkAi0SigvbzS9zObaWiTW13uW11aJt99PgmiPREP4SAAwHMAdkiT9xBh7FibSWiRJegnASwAwcuRIqaCgIMA3zBOOZRrh2muvxYYNG9CtWzdLRoWzsrKiti2B6Nmzp0f46NSpk2e6WnubmpoAyCPlsbo9Soy0k3dmjx496jd/UlIS7HZ73Gwvoc7UqVM9r3lqU25urunjmukOz47E+RBP59xO2c8ZFQ55pLvKkYqDrTnY2VyAONqM2KSwEDh6FGnZ2TCzM82cP/z4VTpShWnu7zebWi0B4JPfUnF0Wxp2JKdg05f5uO7sAkztG+1WwROJUuGOevhw1JW44Oc5ONCU7Z7ujVThx3/mSqCqGbigIRuJrlYkNqZh/17g9Y+L8Mh4pr5d7vXUsSyUN7bCUd0Ml7PBs8ySTCAjMxNobUVahw5+J5jl9z53e3Jq6rAtrRjpzkxUudJ82hNz53heHvDZZwCANHfjsuqBtHo5/aygwLxBa01mJhIa6j3La8vE0+8nQbRHopELsR/AfkmSfnK/nwdZCDnCGCsGAPf/o1FoW1Tp0qULzjnnnJBEj5NPPtnzOpZD6isrKz2v09P1f0h37twJAPjhh1iMCw2eHj16RLsJRAQZ5S6zmJWVFeWWEIQBTjsNGDQI6Bv+nvPhnJKwr6OtM38r8MC3NtQ0A5Xp+ThcB9zzVWz5SKS6h9oak+TffO7xoUaVWxRz2eywubwG4JXNTHW7/vgN8OxPwNM/AvWtwAU/zwEA5NbJUQaJNuC+MfCmykTw+eimQa1ItAEOeyIkd+pNaoK7PbGGmCLdKEd38OPWPSfIZZJnGUEQMULEhQ9Jkg4D2McY43bfEwFsAbAAwNXuaVcD+DjSbWsLiKaY8eLxEQitqifxzumnnx7tJhARZNSoUZg5cyZSUlKi3ZQ2S4LLASmGBd+4IjsbmDYtrB4fJW6fyUM5XcK2jvbCI0uBVkk+91sSkiExhlaXPD1W6P6H6/DqaffAaZNNTG2S1+NDi+zGShRWH0RZprcKi3K7/vgNMGcj4BIWlVvvm1aRngg5SiTZnR8XweejiUMycWF/IC1JdhwpyQQen4jYiMbR44knfN72o2AGgiDinGikugDAHQDeZIwlAdgF4BrIIsx7jLHrAPwGYHqU2hbXfPfdd57XsRzx0Vcxijh8+HBs27YtSq2JDnl5eQCAPoqSf5xYPn4EEYvYXE7PiOr8rXHQsWjn3DcGuPML9aouhDkqm4BElf1Y2RSFxmgw6dQueL0C2LxNDuewSS7YnA7P5zsK+/l9h0dsKM8Rcbvmbgy87mp3BAmmTwc2bwZycky1PSQWLsSgE29EYe9EXJYKzLo2cqs2y/ytwKFVQJP7sLz+X2BKH2BcNyAzOfjlSnSNEwQRAwS8EzHGChlj/2OMLXS/7+8WJ4JGkqR1kiSNlCRpsCRJUyVJqpQk6ZgkSRMlSert/l8RyjqI2O44Jyf7/oKWlZWhvr4ejY3+xlk8iqVXr14RaVukURN8qJwtQZhHYswT8TFzSXTbQgSGC1MUpWMNMSsgFRd7qqj84C3cgyG//YwzNs4HAKzvNhqfDrvI76t1KYFTA/1+LSX/6k6e8si5ucDYsZFJdTn7bPn/wYM4Wq/erlhi/lbgni+9ogcgC0xvrAcOhVAinNHzDEEQMYKRX8nXAHwBgDtQ/grg92FqD2EhsSx8AMDll1+OK664AgCwb98+AMCPP/7oN9+GDXKpQ25yShAEoY73nsc9AojYZ2PnEdFuQtyTk+wrfHAxKSeEUXrLuOkm4I9/BCCLFBIYqlNzkFfrtXJb2+14VTFiT4deqE7NQU1qjuHV5dce8RND0qIQ37x0tySX6f1RLte7rybybTDDkz8ArSoahQvA8z9HvDkEQRCWY0T4KJAk6T3I9z5IkuQA0DZNF9oYsS589OrVCz179vSZxiu9iHCPD7vdHpF2xQqxfvwIIhah6IH4ozo9D9/1OR2vnvK7aDclbpk13v83w+aeHmvUpObAxWxYU3qCZ5pocpooPJkO3LcGdSlZOJzbGb8WD9BdLr/2E52t4CJoZYZsTLG9Uutb4WH+VuD+7d1Q0yyLPbUtwJpDwLaK2L0/HXRHdVSn5fp9dqguwo0hCIIIA0aEj3rGWD7c0YSMsRMAULHqGKVLF69JXFsxNx00aBAA34o1bR1KdSEI41RkdAAASAwejw8iPuBHa1WPk1Cdnuc3nTBBnIh+ks0GiTG4bN7BDFH4+IfC91sCQ4KzFS7mnV9tS1f0PhUAMGrXd54Z3jk+pMzsoJm5BDiUWeQzTQKwbG9UmmMIng60cMg06xceH6cmQRBtHCM947shV1zpyRj7HsAbkM1JiRikf//+ntfxFDHAS32q8emnnwIAcnP9RyEIgmi/JLl/wbxRHsyn8xdLpTwJda4YZG46oc7MJe6wXABZDZWQwOBC7HrdSMwOm1CxTSxZqzQlZpAACUh2eNNd1YYGBv+2yjM/F0Cbk1Kta7QJtFLtRP+MWIOX1y13V9CpScn2+Tzo+ykN5BAEESMEFD4kSVoDYByAMQBuAjBAkqQN4W4YERzDhw/3vI4n4aNfP383dwBwubyjQG0x1WXMmDHRbgJBxC3pSfL/hqR0AP6doSd/iGx7CPM8NgG4chBgd/9c2Zn8/rEJ0W1XvCF2tFNaG1WnxxIuZoNdcmF7kTxYU50aeGCj+9FfdT8XtxsAvusTOyXjk1obAUmK6VQ8Ljg57HIJ68akNB/RIlZFNIIgCKMEtHtijF2lmDScMQZJkt4IU5uIEEhKSvK8jifho0OHDqrT586d63mdkBCt6svhY8WKFQCAI0eOoLCw0OezeDp+BBENqtwDwD/1HIcux3ZDHuv1XjcHQ6hEQESOxyaQ0NFeYJAFShdjsEkulGd0RG9s0YzMOJzT2XBVEB41wu8BR7KKfdYbTVJbGjyv46fUNsPU1W9h/sjLAYQootHzDEEQMYCRVJdRwt/JAB4BcF4Y20RYRDx5fGRkZMBmsyElJcVn+u7duz2v26Lwwb08PvjgA9XpBEFow3PS65MzPNPEEdWcFOU3CKJ9EKuRBfyXzcXsGPLbyoDz/9xjrOFl7+54nO+6xCo3hpcSHuyCh8kDi6PYEJMUVe0PPFMg6HmGIIgYwUiqyx3C3w0AhgFICvQ9IvrEW8SAy+VCU1OTZqc/MTExwi2KHGVlZdFuAkHEHTwnnVdukBjQrXyH53N63iaI2II/lSRIDkNqhNNmDyji8DSpb/qf4/ddTkmmmVZaT3HlPtmjhAGNcVMXUcKh7JJoN4IgCMIyggkJaADQ2+qGENYj+mPEE7W16vHp8RTBQhBE+PEPF2coqjrgeRer/gYE0V7hpWptLqdsWgq5oo8SLlQ4md2TJrKxywjVZV42UP5fnyJ/qUPNYQCyjwjnvijbaWU01SC5tSnwjDFGRWZHS5ZD1bYIgogFAvYkGWOfMMYWuP8+BbANwMfhbxoRKnv27Il2E4KCR3zs2rUryi0JP2IVHhFJkuIuYocgok19su+wrp0uIaKdEqsdzRaV8ZhjGf6day5UuGx2JDlkBfPX4oGqy1T6w/DSuLxcbpItNjw1khzNMXtc1GhJSEaLPfQAbwaQxwdBEDGBEdOEfwivHQD2SpJkQdIfQajDI1W+/fZbzzTRtLUtMXLkSGzZsiXazSCINsH+vG7oUuH1BXJSqgtBxDy8iojqZ7YEuboIfFNXrtQpd1ybmo3MxmpPxEdrFINfG5PTkdpcDwCGTVpjgS8Gn4/iyt9CXs78rcDK5RIcVcB3ybKgFQsiFEEQ7RMjHh9Lhb/vSfSIH+I1NcThkAvdl5eXe6bddttt0WpOWKGoDoIgCMJqYtXcVA2HzX8MjpeidtnkVJf8ujKf1BWtKkAbu4zwE0qiKTd8MWhqFNcePL90GuwXQQfIQoZR5m8FHvgaqGyUj8GBWvm9mWUQBEFYiWbPmDFWyxirUfmrZYzVRLKRhDnsdvnH/rjjjgswZ2yyb98+AEBTkzcfNisrK1rNCSsFBbIpo1pEC4kiBEEQRFukVUihUIv44KWouXiR4GqFi8mv9X4Zq9NyUZku/66KQkmkyXVXlDoqlNRNbW2IeUHKYwIrtDPB0eJ5zQUpIzz5A9Aoj2N5UnwaHeaWQRAEYSWavwqSJGVKkpSl8pcpSVLb7IW2ETp2lPNl463jzMvVfvLJJ1FuSeTIyJDLcHbv3t1nOpWzJYjg2NOBvLcJIqNJ3SQ8VqhMz/e8Vov46CSYm3KMRHDYJJfgSxG9Z6BHxsn/mxJTAQAdaw4hydmi843YQM0EtlgoaXvQxGnlnVfSmE4QBBFZDMvhjLGOjLGu/C+cjSJCY/r06Rg1ahQKCwuj3RRTjB492vP6wIEDOnO2PbZt2xbtJhBEm2DxwHOj3QSCiDosqgkexmhOkMMi1NoqmptyjERwnLB9CQA50sLlTveNhvzBfSx4+3mlmVhHzX8jpbXB89rMWdVJo4Sw1nSCIIhwY6Sqy3mMse0AdgNYCmAPgIVhbhcRArm5uTj77LPjzuNj4ECvY/srr7zieT1kyJBoNIcgiDjitVPuwP68UtSleAMSU+06XyCINkhVWh4AXyPQWCSvrgxV6XJbxYgPXomJd8CdNjta7UlIcDoMbROv6CIx5hFKYkECskku2FyuuKrqwsmrKw88kwr3jQFSFcE8qQnRLy1MEET7xUjP+P8AnADgV0mSugOYCOD7sLaKaJeInh4i557b/kZwqZwtQZijKj0fB/JLfaY9flp02kIQ0aI+RR5Ot7miWMrEAAkuhyfiwykIH5cpKtY6mR2VGfnIqyuDZCDiY3V3b6+aLz9WOO7wZs/r9mDwObUv8PhE7nfCUJIpv6eqLgRBRAsjwkerJEnHANgYYzZJkr4FMDS8zSLaI507d1adzs1aCYIglGiVtTypMz1gE+2HdLc/qOydwdCSkOQzPRYpz+wIu8vpE/GhrNZiNtXlt/weAOT0Gafd3zskGhzL6AAASHC2esxNH/w6mi0yRrU7IicUpvYFHjpJwiPjgR+upXsyQRDRxYjwUcUYywCwHMCbjLFnATjC2yyiPZKYGMNPaBFg9erV0W4CQcQdj02QxQ/+Y2Zn8vu3pkW1WQQRURpa5f9L+p2FZX3PwNHsTj7TY41Fg89HVXo+9uV3R3VaruZ8YnoLL1OrBhd4olnJRYsDuf62eA1x8BTdnJAc7SYQBEFYipFfiGUAcgDcCWARgJ0A2l/uAREVHn744Wg3IWJ8++23Pu8p1YUgjPHYBODH64DfHw/s+p3/qDER+7hiwYghjuGGkQ57ok/KV6wZSfJftbLsTtjSeRj2FfTwlE7NVclMcTEbqtLy4LTZfaI/lHCBJ8WhnjIbi8R6usvBnK5IdLYi2dEMFmLqVKyX8SUIon1gRPhgAL4AsARABoB33akvBBF2eInb9kB9fb3nNZWzJQiiPXG0PvA8hDbxYiR5hUZqmo15S8D6wBiaE1JwLLOj7nK5wJPc0qA7XzRoTEoX3nkFgCd/iHxbjMAFqNaEJCQ6W1BSsRcFdUeDXl48VBgiCKJ9EFD4kCRpliRJAwDcBqATgKWMscVhbxlBtBOKi4s9r10xbkhHELHK4bpot4AIBdJ6Q4MbSeanyu9j1UiSp6aJpCYAT58RWlu5wNOSkIyU1sbgFxQG9ueXqkY8HKyNQmMMwAUol80OJklQ1sW57IPIt4kgCMIKzCRDHgVwGMAxAPrSO0EQhsnN9eY3b9myBQBVdSEIsxwi4SOuId0jdLj48cklsW0k+dgEYPGVcmrau9OARZeH3lb+/d8KeiK7sTL0RloA/wXfl1fqMTgVibU0JI54LGySEwAwZvs3nmnf7490iwiCIKwhoPDBGLuFMbYEwNcACgDcIEnS4HA3jGifdOnSxfP69ttvj2JLIodo6jp//vzoNYQg4hjyiIhvKOLDGuLtOqhvAepa9OdJdjRBgrGBgKbEVAtaZQ2etB5mA5dBxMiPWEtDUoOXRe5+9NfQFkQDOQRBxABGIj66Afi9JEkDJEmaKUnSlnA3imi/FBYWel7n5+dHsSWRo7S01PPa6XRGryEEEcdsI+epuIaS/KxBAgxKBNFFFLoCaTWpLfXYl9/d2IJjqIMtmizHmR7lIbVV9kz5tXhg8AshVZMgiBjBiMfHA5IkrYtAWwgCw4YNi3YTIs7QoUN93kuSRKkuBEG0K6hvZA3xFvEBAE0BSu5+MWQa1pWeEJnGhAn+a57o8Ia3xKq5qciGriMBAMcd2hTllhAEQYRO7BU8J9o1nTp1wtlnn41bbrkl2k2JGp9//nm0m0AQBBFRSPiwBilOQj64ru+UgO0V6vMoTVADTfdZfsydUHJ7ROPVAzFqbgoAKe7ewf68UiS4HKEvkAZyCIKIAUj4IGKOUaNGoWPH9uWf26lTJ8/rVatWUTlbgjCJjZ6r4xq641lDPEZ82DWeRHkFGP6xDfJ7MYVEiw41hzU/i5Wf11i+ZT10svxfAkN+XVloC4uVHU4QRLuHhA+CiAGuvvpqn/f79u2jVBeCINoN1DeyhjgJ+PAc75QEwK7T4McmAC+fC9x9ArDjDn3RIz0x8PSGVqAmgJlqOFDbxFg+5Sf1kv87bAmeaTYXeZARBBHfkPBBEDFAUlKSz/vk5OQotYQg4hPqOMc3dPisId4iPiRYF61VL3iFiNVTfKZHeP+k2qO04hDxnEc2bzehh1DZZf5Wc8uLr60nCKKtQsIHQcQgTqeTIj4Igmg3xFuHPVaJl93Y6BYjjHqSuCSgqkl/Hh45UlBzxKf8rV9ESQR3UqJb+LBJ8VW3SE2n6VhzyPPajDErA8jjgyCImICED4KIEW688UbP6+bm5ii2hCDij3jp8BHq0PGzBkmKjT5mY4BKLTsr5f8uSRYm+Hs9HAFOEqf783dOvAFvjbnRbzogn2eRPNd4Ws1vBT0juNbQ+VXFcHb0zmWe16aMWeMs2oUgiLYLCR8EESMUFxdHuwkEQRBRgfpG1hArkTNalVo4vJmSW/ho0PDdaHUGjvTglGTK/xtSMtCUnO43na8vkhS6m7G12EApmhihyWF8nxMEQcQTJHwQRIxCqS4EYY7C9MDzELEJCR/WECvCRyD48U6y60eoOCXgaD1QkBZ4mfeNARIUy0qxy9PV1h0Jbhgm/69NzY7cSkOkrgWotjrolJ5nCIKIAUj4IIgYYsqUKdFuAkHELd1yot0CIlhqo1Bpoy3ikuKjqkuFO6JgeDGQlaSffiIBcLiAqkb9ZU7tC5zdGyjOkN9nJgGPT5Sni8uKJKf1cL+Io44/j8LhrO82OvQFEgRBxAAJgWchCCJS9OrVK9pNIIi4pVdetFtABMvOSmBkp2i3gogUHdOA3S1AehKQEaCIGQPQ6gKOBRA+AKB/B+DJ04APt8qRIlP6+s9D3XB9+P5h7tf78rpjyN6VQS1r/lZg7fdAVTPwM+Tom6kqx4QgCCISUMQHQcQQYnoLpboQhEmoRxO30KCwNcRLxEeWIHZkJGnPx8kMEBXCYZCDKyo0RBI6zQLT4pT34xVuW5K9GsasgUrazt8KPPC11y/kQK383mwpXIIgCKsg4YMgYggSOwiCaI9Qh9Qa4kVAEtuZpSN88Co13XOMbVuvPCAlQCxzJPdRnBwOH7aVy/v8sQny+9YE7wFKbvEqSoFK2j75A9DokF/z8sKNDnOlcAmCIKyEhA+CiCFI+CAI88RLZy/e2FwWuXU1OSK3rrZMvJibOiWgT76xeXkUhxG4wbHm7JJ6qdZwIR6OBcMv/f/27j1YkuuuD/j3N3Pv3Pdz7+5qpZW0kryrtWRjO14cI1Hg+IHBofAW2BRxYZQEoiQFKVNAGVOQmCRACAnPmJcrQGRih1RsghWK4DKSCRjjB37ptZJWu1qt9nnv3OfcO/feeZ380d13enq6e7p7+nF65vup2p25Mz3dZ7rP9Mz59Tm/g8/c9x3pbTwivyr04PnHDu73mtL2mvm8ONZ4LcxUuEREMWKODyKN2AMfiq05IspQq5Xetjh9Zjx0/9a4vAncMWcEPgoBghlh388RK/Dhs+40g2xKAWNFYL8JXDx6b8dz8z1ym2Rpecf98fmd4FGjW2fcgyO3znQ/RkSUBvb4INKIPfBx6dKl7ApClEfsMEVDTvceH1aAK1Q5JfxHu9Z0f3yrBuykOIPQk8vAPzjRfZVxVIB/+6b0yhFGXFXo/Q8AE+Ybb5m/bSZGuqcXJiJKCwMfRBrhUBeiPmje6Muby1tZl4DC0vErpFztfqylOsv65evur1UKuHPOWDaOTpAvrhszxKTl0gZwegn4D28FbjN7Otw2A/znb9N3dpPzq97PjdcDTK1jOnvamE54odSCwHjfzumFiYjSxKEuRBph4IOIdLHJ4Se5pFv871oFODTRGehwzj7jN9Rpbgy4ex64tBl+20qhY0MKnTPKpOGOOeAdrwC+937g5jZwdDrd7Ye179Jb5sb8bbhl4yqObHlEqDycPQ28/UGgVgDm/klMBSQiiog9PoiIKNd0a+gNinLwi7ukielRYEyzS1rXt7t7fTRVsN4pYT/bF9ZDviBBVg+V2TE9e+KE8bmTb866CEREfWPgg0gj9h4fCwsLGZaEiIZdM8UhARSP+XEj+JElZ/6OZ1aM4IdFKXOa2oDrCxM0qNb9n1cKqUVKrc3kuefUwrhxuzx77OCxkWaPneykAka5iIgSllngQ0SKIvJVEflT8+9FEfm0iJw3b9nqo6FjD3zcd999GZaEKD/W2TOBCIARTMi6B9RTy51/j490Di9R6B7q4iVsXo+58XDLk7+f/Vbjdn+kvWOLzfa0OD/zeNolIiKKLsseH+8DcM729wcAPKaUOgngMfNvoqFiD3ww3wdRMGkmKyTSXZbfHEoZOSLsAYv9RrtM9RawsmPk/FicNF/jtz6Eez93zAYoY4j19WMQZqS3EpGqQru5cO+Npw7u/+GTaZeIiCi6TAIfInIcwD8E8F9tD78TwCPm/UcAnE25WESZY7CDiIj6kWV7+2s3gRfWgCdsvT7s5Tm3AlzbBgoCjJhfd0EDBHuN3svYHZnqfizNfWNt6865FDeagm+8+NnwL+JvGyLSQFY9Pn4NwPsB2K/THVVKXQcA8/ZIBuUiyhQDH0ThDcKVVR25NRyJ/KzsGEGNpuMzaf9qc35enTlB7Lb22689vxauLLd4zJ6S9uki7VlkkjazG3J6HZ6giUgTqef+FpHvBLCslPqyiLwpwusfBvAwABw/fhzlcjnW8m1uRpgvjcgmrjq0u7sbe/2mfKpUKgCQeH3I6/lvrVpAtTqOtbU9FPc47iUu29vjKJeDZ2bsp/5Uq5MoO6f+oFC29gUXlks4NlLHSEafg82tEtZ3C9iqCcrmtED1vQmsre1jst7Czs44NkYb2K4LivstlItNrO0Yn1+34//EjVGMFhT2Rxpo7I6hXN733f5atYDiXgvV6iS2tupYKdcxYrvEt7k5iqYSlMu1znIncO6rt4x6vb6xj+lmE2PFdvl0Vq0aY5Cs43GPGbjZue1W3H7zBfOx9ndRr6+l1vY26qUm6gP+eyav359EwySLSc8eBPBdIvIOAOMAZkXkvwO4KSLHlFLXReQYgGW3FyulPgzgwwBw5swZtbS0FHsBk1gnDZd+6pCIQCmFyclJ1kUCAMzMzABI59yUxzpXrwCTk8Di4iSWPK7yUnjT14GlkDs0av2ZnASWliYjvZYMUgXGysDiIjL7HExvAFf2gdFxYGnJ6DI0NQUsLk5hXwBVAmbngJE6cNcCsDQL7JW8j/9sFSgVgVuPApUXgaWlGd/tN7eN9z45CczOAktL6Ah8zFaM3ihu1TTuc1+9aZTj8KFJLM0Y0wxb5dPZpHkYrONxwYw1HR1/BaYa18zH2vuq127bnZ5GrTSJuRx+t4SVx+9PomGS+lAXpdRPKaWOK6VOAPg+AI8rpb4fwKMAHjIXewjAJ9MuG5EOONyFKJwL61mXYHBc5L7MLQVjmEmmZTBHNYy7XFbb3AdqTeCa0YENx81EpL0GQkR5S0emvF+X1sgLazP3LBhBj7x76vjrIr1OOJ0tEWkiy1ldnH4RwNtE5DyAt5l/Ew0dBj6Iwinq9E2Wczu13suQvsrVbFMq7DWNW/u3WM18rNky/pWrnc/7lbfSR310+ypV4KwuYVm7sVEczbQcRET9yvTnolLqL5VS32neX1VKvUUpddK8DZnGimgwWIEPBkCIgsn6KjeRDnRoaG/smnfMz+SqmbZDBDhnpnhQCN4B4OI6Anf5uF5pT21913yw11Bv3/9q41Z5HIg/eTbFwhAR9YHXyYg0w4AHUTiVfQY/iCxZxj82zXwQk+bQjquV7mXqrZCf14BvaGu/PUPMdAm473D3MnfOpRcU0SAOFYufe7Nxu1tq52CZ2N85uP9LnwuwEv6uISINMPBBpBkGPojCe83RrEtA/eDUufHJ8hvEmkL2VpccpNZX2269M/ChFHDHnPv6CgKcPBRs2w0F7DXMbQEouuyImRIwl9L0sqvVZAKymQ1Hs/02Ofvljx7cdwtuddChKxIRERj4INIOAx9E4R2ayLoERNlSAJYms+39ZLVxnU1dZ5E6Ah8uz9tfVyoG23a1BizvdD6WZZu7XPUO6PRDhwTEs7sbWReBiCi0AcgzTTRYmOODKLzTS8Dabu/liAZZ1t8aVpyhVzmCfr3dMh088BHE86vAqYA9SOIwm1LvkrRN1Kpdj73nE8DfXGn//eBx4GPfk2KhiIh6YI8PIiLqy9WtrEsAjMbYOKJssEd8f3TYfc5jaCU0FXQGQ5xDXaZL7uubLgEjAX+pWvlFAO99sd8Mtq446HA84vblux5wfdwZ9ACMv9/6EeCXP6fwk48DD/w+E6ESUbYY+CDSDHt6UN6ssqcF9YlnvRio7HNIthyt/UbLfTnnUJe7F9yXe3Gjff82l7whdte32+uZKmVfp1Z3kxnqkgbn0EFrX9ZG3LuwWEGPN1z4Kzzw/OMHj59fBzbMgNTVCvCBxxj8IKLsMPBBpCkGQCgvWFMHj1JALcWr4xSPV7nMZJImr14OIp1BGfuPT7/pbbdtiTy/5U7/bRek3TvkHo9ASpquVZJJpJpGT5IT851//9rbjdtnj73a93UPPP843nDhr1yeMQ7wbiPgLDBERAlg4INIMwx4EFHWFICNvaxLQWFl/fURtFHuTMDqVeyZUu9lLFkHfdKSxiGeH+/8++xp43Zzsh1RklZndx7737eXLx7cLygFZSv1tV6zwBARJYSBDyLNMPBBRFlLO9/G3Phg5kRIk9/sKKmVwTyIbkM87GUbs6XWV8q73M6eBz23H27xRAn0Kk8sbL9PiqqzS9jrL/3twf3v+dJH2i9RCi1pNzfcpjomIkoDAx9ERJRrXokRKTpnroak5TUXAnWyegm5DfGo1tv3x+2Bj5i2/fxa92NZBh4WJ5IZ6qKLu5ef6/j7m5/7tOtyBdWCMgMmEyPA+93zoxIRJY6BDyLNcDpbonDums+6BINjxZylcuCuVA8BpUOXDx8rthlQnbO6xPF1V+6eYTVTY8V4p+JNw14j+LLfePGzvs+P1Yys1wIFJUZy2l98S3vYDBFR2hj4INIMAx6UN2wkD44b28bt2q4xM0ZarnPc/0Ar2hObSvAcH2HsNRDoZGSf9jZpefs2XwkQPNodmwIAHN66cfDYbWsvdS33jq9/3LijFJQU8Ll/yqAHEWWLgQ8iIiICYDQK9xrGkIVj0+ltd3U3/bwig0jnhvarj7TvdwU+Yir4+GjvdV7ejGdbg+rUIf+hZ8Vmd7eQd3/hD7oeu7N8AQAgqtWR3JSIKCsMfBBphkNdiCgrew0j+KGU0TWd8kMB2K33XCw1W/vt4SfPrxq3CxPmrW3WkDgDXoOcUyMtd893z+pid3XRMa9wjwNoDHUR/Mmz/ZeNiKgfDHwQERFRmwJaSHdq1Fcd6b0M9fbCetYlaKs1jSS5AmC/aZTt1mmjnTwWY3LTy5tAvdl7uTi2FUYeOzAF6Q3z5ROd2Unf9cVHPJcdbdQg5nxDP/qpPgtHRNQnBj6INMOeHpQ3rLGDpaWMxmmaPxBYh/rnNy1sWhYnjKSebnbrwN87BkyMdj/Xz9dete49C1HWw6fy9nXuF/iweulcOXSi/aBSOL52yfM1P/zpXzCmsy0UoAC89SOeixIRJY6BDyLNMPBBFJFk39AZBArxzbQRdruUb5OjwD2LnY8ptI/t0mTntLaAf8DmpZjzceRtlpW0WcmN3fzst3Y/VlCtrsf+/DXf3fH39F4FhZax3Pl14Gce76uIRESRMfBBpCkGQIgoK2nOjNpSxlAI6l/WXxvfdBw4NNH9uF+x/AJecQcqTh+Kd31ZuOYTnEiS24wsD/31hzr+/v1vfR+ePfbqruVOXX/q4P4fPhl70YiIAmHgg0gzDHhQ3vBK/WBJu9fMxXXg6zd6L0f+dPgcjha6gxUTI/5l8wuy9TOz0KB+k5YDTDkb1b0hA0Nz1c6kMluTCy7RNx1qJhERAx9ERDRA+BM7fxSy76lAyblttvcyPP564GeRiAYZAx9EmuF0tkTR8BMTj6yCR8zP0p9nVvT6DLxodgZ46109hrqkfNwXXYbiJGHSJYnrIPvtt/zkwf2PPfDPMywJEZE7Bj6INMOABxFpgaeiXGl055nMlBXPsL7S5se9l0uqqu3Uux+7bSahjTncs5DOduL0/GqwY/GFV3RnOt0vtSNKy3PHYiwVEVE8GPggIqKBwV4D/bNmdaF8OTSp1zCFrzryttxuDnkpuKSASKrc27Vk1jvI/I7FlNmL5cnbX9/x+Cdf/54ES0REFA8GPog0wx4fRNHwk5NPWUyd24/dOlDZz7oU3Y5N6/UZsKatdZZpabLzb3uPj/Nr8ZaBAbzgmgF6DP3Cm81lpTOD7fZ4dyKXT7zhoYP7f/TGH+qrbEREcRjJugBE5I4BECLKQhaNRUF+EtNW68BuA5gZy7okenL75go6nGXPZWhKP/JSp3QQpHfM2dPA+z4F1EY6K3+z0A6EHJ0EblaBlw/dhY8++C+wMnM0X5FNIhpY7PFBpBkGPIhIB2mdiZQCTi6mtLGYsEEdnEg2PVFE2OMjKc1i53VTew+QL/6z9uMrs7d0BT0ePJ5o0YiIPDHwQaQZBj6IohFhgzQuae5HhXbugDyI8xS9MwQ5KMLurlbIyndzx7u+Znk+eGkzw41H4ExGG0ar0Dn05dff7r7cyQXgY98Tfv1ERHFg4INIM5zOloiylPZV8rzl+IjThfWsSxC/u1xmM7FXKb/6pQA8tRxueys73s+FDaLEaSuhPDC3JjQrTT+f+3qxM3J59rQR/LBm0Lllyvj7L36gjwISEfWJOT6IiIgIADCe0a8CQb6GJTxfbs9SQp2mS52NfudQF7cg180d4Oh0tFwvVyvez+WoSgV2aKL3MlEc9PgIuPzm5ALmqkbkbq802fX82dPGv1rTyB+ymFC5iYiCYo8PIs2wpwdRdHlqPOvo8CRQKgJFAUZS+oWgAL2mIwlgv5l1CfRm/xw6D+2J+e7ll316bfTi16sjy/NBPcAsKTqx9tXCuP9y1vFUMthNiJYajqFoRMNksM9aREQ0NHLWdtbSzJgR8Dgy1bsBFJegM35QftnjDzOlzueeXolh/RoGPJ8tZ12CaHr1zLB29YuHTyZelizVmsCVraxLQURxYuCDSDPM8UFEWTk6lf7wAKWAAk93sbh9LtvtL1eNW/vXl3OoizOvSVJ5OLyGT13fBpo5642RhqDJTa28HV+9842JloeIKG4MfBBphgEPoug0vPCbK0en0t+mctxSdPNj2W7/iJnqwW+oS7UebF2r1eB1IsysLmu7rGtugvaaef8Dxu3W5HxiZdEBf4kRDR4GPoiIaCAwZti/rKYEztuhi6Oure0CN7aBlzb6X5cuNmOcyeRqJXi9UAqYdxmapeMQmH4l9VkJuqvOnm7fr42M4XOn3pxIeXQwgNWHaKhxVhcizXCoCxENmzyd7pSKp/HZUsa/OIMFWTfUnloBTh1q/z0x0n1sX3U4+Pr83k+taUu0CfdZdjiiJTgF9+CRm8kRoNoAfuttP9X1uOu6s66YRERgjw8i7TDgQURZEQBQQHk33W3mKcfHtUo8gZq4G4M6fHXMOhKXzroMvfE71ntNYH0v2LbWdoE127InF7uXYYM7HLfj5eY/vKW7AVEwH3fSoFoSEQFg4INIOwx8EEXjlcyQwvO6cpuEw5PGLDJxcybRjEucQaFBP91b7+9KJdjytSawaQtmuPXiOFg3AChgbsz43I+71NlBPB28uJHQikP0ZDp7GvjVtxuJTgXG7a++vXMYDBGRbjjUhYiIiDqs7gKTo1mXoj/VWnLrHvB4Rd8OZggxb9d3Q+TrsN33m1J5vwmUisDSpE+Aw+OJPAdItxOq1wrhAnFnTzPQQUT5wh4fRJphjg8iytpGwOEGcWi08hVI2GvEt648ve8o7MNa1jx6ykzZAmxh9scLa8ClDZ8FxL13Tt6/WpOK2cSVu8Z13Qmtl4goDAY+iDTDgAdRdPyBnT/nylmXIJxaEyjG8OtpkOuq9d7umjduX7lk/HNz90L365oBspKKAM+t+s9EdHWr93qoLYmfH/xJQ0S6YOCDiIgGAn9g51tehh9MjgJF1rVARovG7XQJGAnxi/OZld7LFMToRWLl+nCTkyoVilsukziIAK86ksy68yov5yQiCoaBDyLNcKgLEZG+7g8xHeuwmzZneXGbccVNmG89e/DJq33aGsCG66mA+zIsATDKVgERDTCe4og0w4AHEQ2dHJ324urtkUSbXJcr1FY5JgL0ThAApw6F34b1VemX50OT3ZEbSU0rrUu9DIs/x4gGCwMfRJph4IMoGvsn5+ZOZsUYCC+nmBsh6FSng2i3AezHmSxVs6+PIOVRAMbMITEtFbzx/eSycVtveTes89rgzkoSgQ/NqiQRDTEGPoiIaOCwwdOfNGd1WTdn3oj7kCkkVw/iCjBs7AGVBKfdzdJoIXyjt1gAjs8aAZBer92tt++7HWY2uMObyvkU1kREfhj4INIMc3wQ9Y+BD2NmjJ0BbVQPijjP8s1WckMVgpq0NZxvn2uXR6F3sMiaTnWmBFiTujQCzO4yMRK+xwdPD+7mxrMuARFRclIPfIjI7SLyGRE5JyJPi8j7zMcXReTTInLevF3otS6iQcSAB1E4O9aVX2k3dNiwMYYAfPpi8OXtjcS0kxwmcdZL8kway7pjrqsTI8ArEkp8GdQ9tl9urz8GHJky7jda7X02O9b5Gjn4r63ZMnrDLFd7b/POee/nsuwJwkShbXk8H+exzETkL4vTcgPAjyulXgngjQB+WETuA/ABAI8ppU4CeMz8m4iIyNfF9e7H/Hp8NANcRR4UzbC/3sX4wT/JLu++4mo8i8TbEM+6x4fddKndy+Op5fZ9Z6BC2W7t94PuG4F3IzXLnl/3LmW3bZ3k+VpOjotORC5SD3wopa4rpb5i3q8AOAfgNgDvBPCIudgjAM6mXTYiHXCoC1H//No7KwGuIg8ja581W8DWfqZFicV5l4AYZUPBvxFpf05skYxAgQ/hkBYiIuotwERjyRGREwBeB+ALAI4qpa4DRnBERI54vOZhAA8DwPHjx1Eul2Mt0+bmZqzro+HTbx2q141++9vb27HXb8qnSsWY9iLp+hC17m5sjKBcjHFqipC2tkool2uo1AQjBYWJEWBtu4DxmnvXjtVqASN7g9/tY68BVCpjKJeDRTFaCljfEzy9OopqtYi1tX0UQ+ynqPXn6EgJq6t1FEXF2tOkvDGGL1xo4BXzzfhWCmCtWsDmVhHlcr33wj5WqwVsb5ewqZp9r8sqV3GvdXCbBWvbq5UCGiWFxpgRetjaKqE+olzf5+ZGEaVaC2pHYXNzFKO1Fjb2BLWmoFVSKI80OtZtqVYnAQCVrQbWSnW0lHQ8X6kJdnbGUC63M/WuVQvY2ipitVzHaNFWhgR++1nnJbs4js3GRhHlYrx1GoinbG5aCljbExR28xWG2m8C65Uiygi2r9l+INJfZoEPEZkG8AkAP6qU2gp6dVsp9WEAHwaAM2fOqKWl+PsSJrFOGi791KGxMWMA9PT0NOsiAQBmZmYApHNuirKN+SaQZVWd3TK2X9oHRgrGMI3dErA06758rQIszaRbxizs1oGZdWAp4JttKQBVYLYOTO4Bi4tTWJoOt80o9efIFrB0yBimMVUK/XJPMzeAien462ZzG1hF/+ttbAMzG8DsTDxlbG4DS9Pt2yxY294ZAebH28kyp9eNpKVu73O+CRyaBhYngNl9YGEaaFaNHB8LtuPnfF+TRtwDs7PAwqLR68P+fGkfmLoBLNkebG4DcwpYPASMOX4Bx31+tc5LdnEcm6TOt0nVm5YCVBVYmop/3Unaaxj1eOlQ8NfwNxuR3jJJvSQiozCCHh9VSv2x+fBNETlmPn8MwHIWZSMionwZK3Y/5je2f5hmfIkyYE6pfI/Lp3xRjlur6k2NIlAFFuF0tjrjcSAiXWQxq4sA+D0A55RSv2J76lEAD5n3HwLwybTLRqQD5vigPNvMIDfEKdsVuSBBjSGKe4RyfrV9/675zIoxNPYbgxtgUkBHi9eaqtaLM0+HdT9IslY52GC37Qync77/cHbbJiKibln0+HgQwHsBvFlEvmb+eweAXwTwNhE5D+Bt5t9EQ4cBD8qzyxvZbdv+yfHt8ZF4SfJHKeDmDrC2a4xtH89gIGwSx0XnY/38WtYlSI8CsDDh/pyg/dm1kqAquAdLnlnxXn/QrgVpfcMW+VVORKSV1H/aKKU+C+/vnbekWRYiIhpMfg3eYRrqElS1Djy7CsyNAZMjQDXlXLV5i/fGUd6Dhv0Q1EelgDvmeizjWN5tJpi4pqIegl2uF+5wItJAJjk+iMgbh7oQ9Y85PsKz9ssTzLCVCoX8BXyCcvbW8JvO1r4frDq4tmvcBt0/vYbSUHbyWsf5PUE0eBj4INIMAx5E0TkTJfotM+hCdf+X7ivu5I1n6XCCJMy16pwIcH7NGG51JIGZQPJ+7M6vZ10CIqJ8YuCDSDMMfFCeZdletn90ONQlHGvIRZannySOS9yrPD9EeTni5Nfjw8rx4TxWxQIwOxZs3X7rHzR79axLQESUTwx8EBHRUIkpTcBA8ZoSNLXt52SdVqMzaoBoead93wr0hNnvcQaHlneASoyzMO01gIb54XIGIubHg++zYQlgRMGgbTo29rIuARElgYEPIs0wxwflWda19qAx2SPHx/Or3s8PkiDHw2r8slGVvJvb7fsixnStzRD7/YV1I8AQB3ugIg7lqvd01rfNBMvxYWef7aWXIENpiIJ6eTPrEhBREhj4INIMAx5E0TiTKXpRiK/xOAh2bF3n7dOKpi2R6WxjXqm1uqhn6YsbnX/fPQ+MFUNsX+Pg1GjBf9YVv6+2g3qngGfKwV5jCbtP8vwVO8gJcXWyXee+JhpEDHwQEVFfdPxt2KvHx7my9/PDpmXuq/1m+7G0G9h5aWA82eeMNzu1zr9Hitm997i3K9KuS85Go199EscyHevw2d79h23r77EsURjbtd7LEFH+MPBBpBn2+KC8UR73sxBk+0rF28VfV0oF602glK1hak0rmlipfMqRwTajius0LdC7F0cYAu/8OT0DE+aTVyohttejrg5iguO8ljtvuJ+JBhMDH0SasQIfit+8RKEEvcL88lbyZdHFqUO9l3FrrKbe4yPdzeVWrdl7mayMFoG7F9yfCzSdLYC13fbfguABpmEZlsCeLengry+iwcTAB5GmGPigPNLlR7nfp8feuBpkQRuDbqeae5diL07qTi/lowETpozPaj5Ey+phpFTnuaAFI5Grm5OH3Hu+9Kq78wGmuiXqhy7fZ0QUDwY+iDTDHh+UN0GTiqbJN8dHesXIBaXa++SOWeP23gA9ReIvSLyrGysm13CJut6SbejRMDWqnIEQu/GR9n17j5Ze++eOOeO2Wndf/yB+hXL2mnR0DP8jooHBwAcREUWm24/DoDk+hoFfY7NjOdv9UogZRuKUVGMuqUMdtbjOgFKWOT7S3G6vTYnLMkH38YrHNLp+0wQPySmAImL9IBpMDHwQaYY9PoiiCTOdLbW1zKubCxPZXk0etuMyOWrc7jWAy5vZlCGpfe42zMp3OlsJH4gpm0PWlDL2n3P9ixPAq450vy6r4F4crBwf/HmQLO5fosHEwAeRZhj4IOrfIM7okBRrdwzifon7PTWtTLARAkTOspxcNG5bqnMqYd91hN+sp0rMU3b2u69v7rTvH5pwzwmyZSvzdXMGmGo93HZOLwE3t8OXTwcX1oD6EMxIlbUBPBUSERj4INIOp7OlPFGAXskKVO/x2QrA7bOplSgzQWeAaDn21etu8U5EmSdJnEpXE0iMm1Qjq1fj/qWN+LcZdZc7h7rcs+i+X2ZK7q8POqzLYg+y5MkwTMNt2Wtku/1hmSmIaJgw8EGkKfb4oDyK+ypyGGF+pM6PJ1eOvLEnNwW8r7anUY6Bl+J+XQ7QuH8poSE2XYGIAIEJ6/iHDWKElee2rILRw+VaJeuSJO/8atYlCE+p7kAyEemDgQ8izXCoC+XZpY2sS2A0DnT89KR9tTbKdLZZNApPHcpfYzRKed3qZJZXlEWAzb3stm8n0m4wrlTD7ZdvOKphz7OEKAUcneqc/YbiF/Xn1/oecDWloFSQwCYRdWLgg0gzDHxQniR9dXaQlKtZl8Bd1meaMTPZZBLlSGxWlxgrfZhT/VoCQ210YE/YubFn/O3cxdc9hu6MFmy5VwLKus5HpQAcmcpv+fMiD/v3uXLWJSDKHwY+iIgo1+zTViroG4xJPZYZdDpbW06UrHogJLFdAbRrwTjfZti3HSaRZwbVrYP9mAbJjWKPXYh0r2/FI3CoYAzNCrov85y3oaUGI/9OEFl+dA/OhxFem9bh2c04BwpRHjHwQaQZ9vigPNGhlj6z0vm3AnDDJ7FjVu0GXcd+n1vtbHQOkqdWei+TlmE4pXsFFa5W4pnOdsNlaM4w7Fc7ZyLYQfV0hp9dK4Ae+nUpHphhqANEcWPgg0gzDHxQ3uh4BfXJZe/nRjL45nMmENVJvQlAASfms+0pk6dTXlz7iVdt24Lkmnl5q31/pQosTrRz+oQ5Jnmqa27yXv4ghuE99oP7hyg8Bj6INMPpbImCq9aBHVv3f6V6j/d/5VI2Dfwshh4EPZ20lPdUoWFFmSY1qeNx3+GEVhyR83i8sJZNOZL20mb4pLktx/Jur7Gv88a2kegzbAMw99+wuX8D+stDUKHXzD69pnUnGkYMfBBpij0+KA+c+TRumUp3+3uNdsJHqxx+WfWVAi5vZROE0PUjLWg3OuOIu27uR3td3LtHJLkfOVH2Uxzv70jMn6+kquRTy+HXbS1/Yh6AS44PoDvPhwhwc8c8D4U4Jpp+FHuyerbktfx5EXX/hu151I9e59nr28Fy6xANEwY+iDTDoS6UZ3E3zHopSnfuDL9PjkI2U3hm8XEOuk23RJJpy1tHt6yKa82AE5c430c/ddwefJsphUhUCuCmTz4f19fkrK45Bc2HkneZJjc1b/NcVYahjhCFxcAHkWYY+CCKLkjPCgkxA0RcdO7xAeidg2SQOINux2aAexazKUsSon6uxBbAfHkreHCiINFyfOSVrjNWpaHX0I5Y8XxINJAY+CAiosh0aHBY2xcBng7QxX5N4+6/cQZHgub4sHedZ3LT5CjVmZwTAJYmgMmR4OuI0lthp+b93JUt7+fCurjR3+utw7+x510P58c7/45aXxs5ncYoTN6eQbPqMZ1xEqKeii5txFkKf7fOpLctokHBwAeRZtjjgygc+yelBf8GdGUfqDWTLlG3oD0qlncSL0qXO+eN8mU5fWTe2nL9lLcSMQeKJehXg5Xr5sJ6+7HzjmSq6zEO+9r2CbD0InAkQ3XZwQJg3BkkEiNZ8aUNoBF0aBeAr1yPVMzsDXGPjzR/EUX9+VXp4zMQ1kSPgOmwDIkiCoOBDyLNMPBBFN3lTf/nX9xIpRhdgg51ceYrScPcWLt8WV5NTuKtp7U7g+aZUKq/q8JhDo8z6S8A7NpmQBopAHfORS+LU0sBRduvyrBfYS0FLE0a973ep/Pxjr+H4CtzsgSUYs7zQt0OesCFPB+meQ4dgupOFDsGPog0w+lsKU/cfuhlGbMrV/W9yhWkWHEWPWg+gDivDEadQjGJ015LAfuN+NcLdJc3SE8dBWOmBTfVoFeKIySi3aoBV12GtNw6A0yOhlyZj9tm2vVtpuTSO8OHlWD32LTRAynM64bJ0gQwNZpNguZhkoecR7p+zxHpjIEPIk2xxwdROFYbqNesLgBwft1noQQE/SGdRY+PjulsHc+FPQ3tNoALGzEUKgb7DeBKAgkR95vAoYn41ifSORzFd1mEPyZKAU3Vvm9ZivE9AMDCeDsQMTHa2fvj/sO9X2/VwVmPWV0E3YEOe90NEuVrtAYjWHI5xtwslE85TVNDlCkGPog0w6EulCddyU1TnhrVbVtBrmL3m2chrKBDXWLt8QHj2GzueV8hXtttz4zRJUIDsdBH75G4602xkEwSy+dXjWEisQq4r+2JaHux90Kxjsnjl9qPvRBj8M95zJ1vp9Dj/dkDOgrtIS9uy3X8HfJ8c67s/7xfItgw+PWdb7k4fLkoJJFeGPgg0gwDH5RnUa5Ix7FNu7EAXexjb7gGEGioSwL7bq9h/HNjzephH9N+LWC+Ci/HZ8O/xq9dHHaf2INa9x4KXxY/0yX3x4MEtuI4tv0M5XJOB2rP99Gv82vedSwIazpbqx6MhMhjMR5jzouLcfYES6hnySD0WNFdP5/VtL7/0h46STQIGPggIhoS6ylM4xr2Cmy/7DlGgjQIrB+lurYdYp3O1to3PY6JM1g1ak9QGWGbC+O9l3N/sfvDYWebsScPjTsR5JRHb6KbO8BKxOk2lULgHR12RqLnyu0pdJ29nPab8XxWb24Df3a+87Eo623auo+5fT5rre7gigA4aQa3xnoc62YLeG41QsEiWPaoC/0Eh+yG4bpIllMOR53eu1RMMblpjzqg63ccUZYY+CDSDHt8UFLi+tFtt7VvNKAsWfT4sBxs17H952zd27fMxl9SP0693nvQpJ9Z7DpnctPDPjNrtHq8D4Vo+9bvNVnkPfGSWFEC7rPjs+HKsN80hjm5veb5VaMXyH4D+PTFECt1uFoxe7xEX0VHrg6v+rVT655+177b3JKpLowD52yBs7Qa0zcq7ofUOZ1wVMPQ62MtgUB9qCGOESr0XgMoajqry/MpBf2IdMbAB5FmGPigpCTRgNzYMxJaWsLkILBrRmyQBNmW/Sr5jtm9/0afwzm8POXROyFoQCCJY3RpA6j77N+CABc3gq3LGehyUqo9fGbY9Dp01vNbtej1PWiPqqrLMJaayzardaPOBZ2O16tMQGdD/9aZ8OuxZiHqyhvUY9t+X5W3z7aDHQrAXfPpXAn3KhK/1bMVdCrpuH5+NVtAPWQvraDCflckNcMVUZ4w8EGkGU5nS2626/332U/qYqe9xkadGvWZkMMZ7E7FnMchKuUzfWrQqWWTSG663+h9TLySnzpf16vnivJZ16Bw61nw9HLw11/aCDb9rRsBAlUSK4nnYy+2A1VuPb6OTfdf59zqddgZYw5yfIj7FNn25ZyCll8pYHas8++o6wrCa2aaOAzCdZHrPjMuZf3+4tp8peY9hXW/st5HRHnEwAeRptjjg+x2m4W+eykkVaXEcT/tHB9WQ7TXzBFJ228Gv6LoJdYcHwjeQ8CtR4rb7lTwv9I4DKetk4vdj4V92/Z9GLqHQ49lXlg3hndZQ5a8pioGgMNTATfsV6b+VwHANtQlxLZGCr0/984RcGlcW8jjxyDolMpxKfvkxNFl/4WtK27lTizwkcxqiQYaAx9EmuFQF3KjAKxEvEpsSWIYhbK12qr1bMaeW5ssmt9ofsM6XtpIujTugg51abbchyn0Y7THtK5hEtIq5b9s1BwfeeH71kJ8vpqOZY/GEICwWLO12AMCSrX//n8vxbetONkDQG77+WWXIVSHp4DFEL1L7HUzyW9Yv14ruor7vBO3nO3OA/0MI+sXf0USdWLgg0gzDHyQ035TUG/1f7pOOknkhjnEIc2q67Ypv0a+PT9F3OX0a+wEHepSbxlJHOMpkLFNr8CGlfvEGj7xyiXXVXT9PcyzCSi458oI+56d+/BIiMCH3xABu3scPVOsuhl3DhZrvf1+nHZtgdOgQYMwyZTtn8GC6JU0lzrp8PMnriIk1ZOm53nY8RmKK7EuUZ4x8EFEpLn12ih2GsW+f4gl0uMD7cZEU4XrPRAXt0aSfSy/U68pTpOY/Saoloo/F4tXI8+a7cYanjTq+EXgmk/BpceHPe/Hdi37K91JD2mwTxFrOTod/PVKGZ8V+9A152wlXgTeU6U6TbjkInFztdK9r6LMAGFfRZRzwEub5mt9XhzlkFqvOVdun69KRfftDHLQLk/S/g550SU4YdWPpBJh9yvsPsrye41IFwx8EGmGPT7ITRy1IYkaZe/lYFXZVHt8uCUoVMCdc9HXeT5goy9MwyloHoemQiIHyrcxKcDlzYDrQXfxLm8CL24Y9y+uR288vhSwDL30kyg3qA1HoOLIZPDDNj9uHOcVM4ChAOwFHGYwXQr2+XIOW/AaRiIAVl0CKWFmgAhyvE+79CbyWpffZ8UZoDk65Z5s1s7aXfaeYF65iNweu2brYROmt0wWQZT1BKaATcpB7hWPc3iatn162T3+YnrlIKJkMfBBpBkGPshJAfjK2qzvNKJBtJKa1sVav+qdA8KL2/j9Xqp1I2u+VwPDuopXdjQG4mqQPFPu/DuO3BfNVvw9c3pt9qBB7NHDo+Nvl8f2Gsa/5WoBBWmvJ+zY9q397sf8GiRenPkzdGHtt5FC99XXoGW+ZyHY56tS65zG2SuYcDDVa8L7bDTgpFR+ddHNVCn4uu2KheDv2R4YChpYyKoKRjmPpu2qo4xuU4Cntf9aqvNz4ixDoc8ejFHOX5ZYjqWt8PcGDD4SDTIGPog0w+lsyctan1fzkhjqsjgJ3Gp28++nx0fQrv52L6y1exrYWZt/ecvIh/DyZmfjZarUuVyU/fpcOWQgKWCOj5YjcOR2NT5unlOHuj2ouo9vU5lJWRtykEBTqehTttpZwSuvxolTZT+e7YYWYhrnc2Wgbr4fK7dH0M9mmK+HVVu9VgqYNod/bdoCTE8u65fUMspQF68eJfb9as8fksbsU0l8lW+bx2rM1stF16EYXqzz7cHuianHx3Pl3ss4V7vXcO9p1lLtJMFhnJjv/Ntv5ppe+u2946x+Jbb4iBj4INLN3XffDQC4//77My4J6cL6EdjvD6GrAZMihmX9wLfiAGldrfOaAeLmjpFQbrtmXPle3e3sor4w3rn8F6+G2261DlyptBshroWKqKk6G2vXXI5ZPWTPH3svDDfOp3pdaXQe36Yt4GVt5+kV73qw6dKzw0tLGccySKMG6MyP8ly5PZwkaX7JMj9zqfsxa1krN0iYoGSvRqHb0A4FYHrUuG8fxlJrGg1nZ/24WjGCWUGMBcwlEsS8OUNLkKEu9kbm8Rn35Z8pu+/bSs19P55fiycXQlI9aKwhVqfMxLVP3Ox/tq+s9OohF5ZbzxEnZ73ySo7bVEYPtLCn81kzqG59xpL8LpzzyWPlRtOOcESpYuCDSDOHDx/GBz/4Qdxxxx1ZF4U0ocyfX9dtV/bchgX0kkgj0NaToWX+WEzrB5Y6+K9T1WzU3NhuZ9T/68udDT77VJ+rIQNKG3tGTxJnA8mvsaMAXNrszg3hFGSoy7mAQQDL7bPtH+R+rF1i733TlcgU3e/Tnh+kYjYW/N5DmCmFFYz97JyRQKnePRX2m8FnQOmXwAj2uLFyjth3iX0fKuU/E5FzO70+X9YVdft7ny119oSxDzdQ6O4ls7kfPBgTtgHm54458/31aHE+WwZmAtTpiZHuYUTWqleq3blMwswS47Sy0z4nBM3pE9bLjt4J1yr5adCu7brX87jKHyVg5RUQbrWAhorea8dKDpzkELI7IuSx4ghqGnYMfBARac7tt8qljfDrSXr6RqXMhn2E7UT5fVmptWeS6SgHgN2G0XizfgzXW53jrV91xLjda8QbRPJMbqrMWSV6Xa1X/se2FfLHeJRu8PcdNm69cn4469HabnuIzk69HVSKY4z6jhnc2HU0am7uAF8I2VMnSSLewYtas33cF8weDQqdAbeg+XskQOTDOj4VR34Be6PcOQymazu9N9MlzTZV0KFP9x12H5I2VTJ64jj3UUGi54jZrgUvV1TOz3PQgJkOrm4Zn1kFI3AVp6gNeq9hcS1r+F6fw8D67Q3Vz1AZpyxmXCPSjXaBDxH5dhF5TkReEJEPZF0eIqKsuf2oi5ITo5+rmYHWL0YDLq0fV1+86j6DR0sBJxe7H7f3GiiaORlqzfBXCvu5ktsraKHQ3RizH7OW6uyt0kuvXj723hpWo60YoIx29il47cMjNiPUUadLG+4Bu6KEH/ITF7f6XfB43GI1pg9Pth+zD2O6uB5s2wLg6R6NRq8Apz24Ym/cuy0uEq7RNz7imM424knAOke5VcFXOvJ4BJkppiCdw5/seYjWdruHXfkNWQL8j3FHw7KP3gJ+6o5AR63V/ZiOFIDnVttBXftn1203Wccp6LTKTdX7vOXGKwFzC0ZQyS2HVBhBeiX5ibPHWq+6TTQMtAp8iEgRwG8C+A4A9wH4RyJyX7alIiLKlttvlTDTKlr6uZoZpCFTsAIfjm78gdYfrViuXcp3G0DJnOlBHMsCxowOVvAgyhVTrwZNkDHrvX6bdwU2bI2pRstoIL+8aYztB/x7VTh7STgtVzsbfs+tdpfRefy8rho2W8brGi3b9MYe2/UKwLkGFDySht7Y7g4QpaHWdG88FAr+OTEarc4GfZg8J069cm94febsvRHsQRe3AEdBgD/4WuiiHYjS6L9r3v/5B24HZseM9/fZy8GG7hWlvb/cZoxxJjZ2axwGPTfd2A6xcETO89VuHbiw5r6sbqzAW5CqYe3GoEHpRsuYLSkuLbNnW7+xq36DX/1WJ/vrk77wQZQHMaakisUbALyglLoIACLyRwDeCeCZTEtFRJShK9V2Nk6rwVRrhmw8KSNHyPouUPI481dqglGPdT5Xdp8Ob6duXO21hpXUGkaDtFAw7l+rACcWehev3up+P9crwDGPpIV+Lq4DrzaHstgDA9YMIQKjgbO13865Yd/25r7/vrWGxuw3Oper1owf4G6v3a4ZjZRKzSjA5Q3gjvnu5fbNZJPWOp5ZMRI3FgrAuRXg2rbRPbvaAO6cN7qPz7rkWLiwZhxvgdE7aKzYXa5LG8Z+qJmNKWdPEmv/HLL1UqjWjR/T47Y61DC7hIs5re1Uy6gXXnV0r2Gst1Bovx5wX36vYTRci4XO565UjN4qzuUvbxi3G3tGN/HZMbPnSUxX3zf2jKu406XObStlvGeverOxZzSyrcCjvafSU8vd6/NiNcrdlrUalkWXBuBOvTMQZs+zYgU+rHVW9tsJM4OUabdh7N5egbZerNlWWubVe+chGx8x6sPNHeO9Bpl1o1hoD3vbb7YbolZdtz5r1rmv1jKTWto2XrUd16rPMb5WAW6bBSDtfeLkdX4Iq7wL3LJvnFdmx9rr9KuDvSQ5DXS13h7ytWM7bpv77mXeq7dfZz237/Odt1c36o3b88s77dmT9pzn7AYwUu8+lzdbRnmbKvh3baUmKJg5Zarm+qyeLaGPier+XDp5vd92eYx67PzN4Db982gBmBwNWUaiHBKlUfhPRN4F4NuVUj9k/v1eAH9fKfUjtmUeBvAwABw/fvz1X/3qV2Mtw+bmJubmImQMIjKxDlHcnn7mHFpKcO/p0wc/TqN0Wx0RI2Gbl62tLczOzoZap8D40VRrGWUq+OQ68BPl/RTN3gelQruh0jST0o0WjHJYPSgUjB/dBWl3ia6ZvRQk4rbdGgp+76PX/rdeD7TX4cy1YJVXqd5XA8Usp3U4nOWyGmbFgrG+orTvW9vYa7q/xr4q6z1vb21hbm724G/n1LxeRgvG1dq9RvfyXu/X77hZ+7lUMJ7vtc/DGC2YjXN01vMRsz64bcpeV8aKRh21eiqNFo2gnyDYkIVSwVjObTsjYqzPvn6r/tfNnjhF2+ezVDT3T6t7X7odZy/W576ljPcHdM7wE1SjZdZX1V6fPYijlLEfrf1VKrY/L17bU8r4nFvBlNFC+73Wmu371rkvSm4Ti/O148XuMtVb/eXlGBHjs1Kzn9sCfs6CrNutURwHe0/AoGV1nmO9zrmWIN8hQb9nrO+WAozPepDXbG1tYWFutuO8Z52D4zwHWUYL4YY5+b33orR7Serq8OHDX1ZKncm6HJRvuvX4cB3q1/GHUh8G8GEAOHPmjFpaCjDIM6Qk1knDhXWI4jQ3a3R7uPVosvWqPMK6S9Gx/lBese5Sv1iHiPSnVY4PAFcA3G77+ziAaxmVhYiIiIiIiIhyTrfAx5cAnBSRu0SkBOD7ADyacZmIiIiIiIiIKKe0GuqilGqIyI8A+BSAIoDfV0o9nXGxiIiIiIiIiCintAp8AIBS6s8A/FnW5SAiIiIiIiKi/NNtqAsRERERERERUWwY+CAiIiIiIiKigcXABxERERERERENLAY+iIiIiIiIiGhgMfBBRERERERERAOLgQ8iIiIiIiIiGlgMfBARERERERHRwGLgg4iIiIiIiIgGFgMfRERERERERDSwRCmVdRkiE5EVAC/FvNolAOWY10nDhXWI8op1l/rB+kN5xbpL/WIdStadSqnDWReC8i3XgY8kiMjfKaXOZF0Oyi/WIcor1l3qB+sP5RXrLvWLdYhIfxzqQkREREREREQDi4EPIiIiIiIiIhpYDHx0+3DWBaDcYx2ivGLdpX6w/lBese5Sv1iHiDTHHB9ERERERERENLDY44OIiIiIiIiIBpb2gQ8RuV1EPiMi50TkaRF5n/n4ooh8WkTOm7cL5uOHzOW3ReRDjnX9uYh83VzP74hI0WObPy8iL4vItuPxHxORZ0TkCRF5TETu9Hj9mIj8TxF5QUS+ICInHGXYEJE/7XPXUAA5rT/fIiJfEZGGiLzL8VxTRL5m/nu0n31D+ouz/trW+aiIPOWzzdeLyJPm+es3RETMxz3rpeP1PP9pIqf1h+c/0q3u8rdfzuS0/vDcR5Qw7QMfABoAflwp9UoAbwTwwyJyH4APAHhMKXUSwGPm3wCwB+BfA/gJl3V9r1LqNQBeBeAwgHd7bPP/AHiDy+NfBXBGKfUNAD4O4Jc8Xv+DANaVUq8A8KsA/qPtuf8E4L0er6P45bH+XAbwjwF8zOW5XaXUa81/3+XxehoccdZfiMh3A9h2e87mtwE8DOCk+e/bzcf96qUdz3/6yGP94fmPAL3qLn/75U8e6w/PfUQJ0z7woZS6rpT6inm/AuAcgNsAvBPAI+ZijwA4ay6zo5T6LIyTmHNdW+bdEQAlAK4JTpRSn1dKXXd5/DNKqar55+cBHPcotr1sHwfwFivyq5R6DEDF6/1SvPJYf5RSl5RSTwBoBXmPNLjirL8iMg3gxwD8nNf2ROQYgFml1N8qIwHUR2zrDlovef7TRB7rD89/BGhXd/nbL2fyWH947iNKnvaBDzuz2+DrAHwBwFGrcWneHgm4jk8BWIbxBfTxPorzgwD+r8dztwF42SxbA8AmgEN9bItikKP642dcRP5ORD4vImf72D7lTAz1998D+GUAVZ9lbgNwxfb3FfOxMHj+01CO6o8fnv+GkGZ1l7/9ciZH9ccPz31EMchN4MOMuH4CwI/arryHppR6O4BjAMYAvDliWb4fwBkYXRddF3HbdJRtUTxyVn/83KGUOgPgPQB+TUTuiVIGypd+66+IvBbAK5RS/7vXoi6PhT138fynmZzVHz88/w0Zneouf/vlT87qjx+e+4hikIvAh4iMwjhxfVQp9cfmwzfNrmVWF7PloOtTSu0BeBTAO0WkaEsY9O8ClOWtAH4awHcppfbNx37eWoe52BUAt5vPjQCYA7AWtHwUrxzWH79tXzNvLwL4SxhXMWiAxVR/vwnA60XkEoDPAjglIn/pUn+voLMb7nEA13qUj+c/jeWw/nji+W+46FR3+dsvf3JYfzzx3EcUD+0DH+b4yN8DcE4p9Su2px4F8JB5/yEAn+yxnmnbyW4EwDsAPKuUatoSBv2bHut4HYDfhXHiOjhZKqV+2lqHS9neBeBxc8wfpSyn9cfr9QsiMmbeXwLwIIBn/F5D+RZX/VVK/bZS6lal1AkA3wzgeaXUm5z11+z6WxGRN5rb/oEA6+b5T1M5rT9e74XnvyGiU93lb7/8yWn98XovPPcRxUUppfU/GCcaBeAJAF8z/70DxrjJxwCcN28Xba+5BCPKvg0jCnsfgKMAvmSu52kA/wXAiMc2f8l8Xcu8/Vnz8b8AcNNWjkc9Xj8O4H8BeAHAFwHcbXvurwGsANg11/32rPfxIP/Laf35RvN1OwBWATxtPv4AgCcBfN28/cGs9y//5aP+OtZ5AsBTPts8A+ApABcAfAiAmI+71kuX1/P8p8m/nNYfnv/4T7e6y99+OfuX0/rDcx//8V/C/6wPJRERERERERHRwNF+qAsRERERERERUVQMfBARERERERHRwGLgg4iIiIiIiIgGFgMfRERERERERDSwGPggIiIiIiIiooHFwAcREVHGRKQpIl8TkadF5Osi8mMi4vsdLSInROQ9aZWRiIiIKK8Y+CAiIsrerlLqtUqp+wG8DcA7AHywx2tOAGDgg4iIiKgHUUplXQYiIqKhJiLbSqlp2993A/gSgCUAdwL4QwBT5tM/opT6nIh8HsArAbwI4BEAvwHgFwG8CcAYgN9USv1uam+CiIiISFMMfBAREWXMGfgwH1sHcBpABUBLKbUnIicB/A+l1BkReROAn1BKfae5/MMAjiilfk5ExgD8DYB3K6VeTPO9EBEREelmJOsCEBERkSsxb0cBfEhEXgugCeCUx/LfBuAbRORd5t9zAE7C6BFCRERENLQY+CAiItKMOdSlCWAZRq6PmwBeAyM3157XywD8K6XUp1IpJBEREVFOMLkpERGRRkTkMIDfAfAhZYxHnQNwXSnVAvBeAEVz0QqAGdtLPwXgX4rIqLmeUyIyBSIiIqIhxx4fRERE2ZsQka/BGNbSgJHM9FfM534LwCdE5N0APgNgx3z8CQANEfk6gP8G4NdhzPTyFRERACsAzqZTfCIiIiJ9MbkpEREREREREQ0sDnUhIiIiIiIiooHFwAcRERERERERDSwGPoiIiIiIiIhoYDHwQUREREREREQDi4EPIiIiIiIiIhpYDHwQERERERER0cBi4IOIiIiIiIiIBhYDH0REREREREQ0sP4/RbqHxlJLhzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(y_pred, actual=test_data, history=train_data, interactive=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
